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  • 151.
    Jomaa, Diala
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Yella, Siril
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A comparative study between vehicle activated signs and speed indicator devices2017In: Transportation Research Procedia, ISSN 2324-9935, E-ISSN 2352-1465, Vol. 22, p. 115-123Article in journal (Refereed)
    Abstract [en]

    Vehicle activated signs and Speed indicator devices are safety signs used to warn and remind drivers that they are exceeding the speed limit on a particular road segment. This article has analysed and compared such signs with the aim of reporting the most suitable sign for relevant situations. Vehicle speeds were recorded at different test sites and the effects of the signs were studied by analyzing the mean and standard deviation. Preliminary results from the work indicate that both types of signs have variable effects on the mean and standard deviation of speed on a given road segment. Speed indicator devices were relatively more effective than vehicle activated signs on local roads; in contrast their effectivity was only comparable when tested on highways.

  • 152.
    Jomaa, Diala
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Yella, Siril
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Review of the effectiveness of vehicle activated signs2013In: Journal of Transportation Technologies, ISSN 2160-0481, Vol. 3, no 2, p. 123-130Article in journal (Refereed)
    Abstract [en]

    This paper reviews the effectiveness of vehicle activated signs. Vehicle activated signs are being reportedly used in recent years to display dynamic information to road users on an individual basis in order to give a warning or inform about a specific event. Vehicle activated signs are triggered individually by vehicles when a certain criteria is met. An example of such criteria is to trigger a speed limit sign when the driver exceeds a pre-set threshold speed. The preset threshold is usually set to a constant value which is often equal, or relative, to the speed limit on a particular road segment.

    This review examines in detail the basis for the configuration of the existing sign types in previous studies and explores the relation between the configuration of the sign and their impact on driver behavior and sign efficiency. Most of previous studies showed that these signs have significant impact on driver behavior, traffic safety and traffic efficiency. In most cases the signs deployed have yielded reductions in mean speeds, in speed variation and in longer headways. However most experiments reported within the area were performed with the signs set to a certain static configuration within applicable conditions. Since some of the aforementioned factors are dynamic in nature, it is felt that the configurations of these signs were thus not carefully considered by previous researchers and there is no clear statement in the previous studies describing the relationship between the trigger value and its consequences under different conditions. Bearing in mind that different designs of vehicle activated signs can give a different impact under certain conditions of road, traffic and weather conditions the current work suggests that variable speed thresholds should be considered instead.

  • 153.
    Jomaa, Diala
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Yella, Siril
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Speed prediction for triggering vehicle activated signs2016Report (Other academic)
    Abstract [en]

    Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.

  • 154.
    Jomaa, Diala
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Yella, Siril
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Triggering Solar-Powered Vehicle Activated Signs using Self Organising Maps with K-means2014Conference paper (Refereed)
    Abstract [en]

    Solar-powered vehicle activated signs (VAS) are speed warning signs powered by batteries that are recharged by solar panels. These signs are more desirable than other active warning signs due to the low cost of installation and the minimal maintenance requirements. However, one problem that can affect a solar-powered VAS is the limited power capacity available to keep the sign operational. In order to be able to operate the sign more efficiently, it is proposed that the sign be appropriately triggered by taking into account the prevalent conditions. Triggering the sign depends on many factors such as the prevailing speed limit, road geometry, traffic behaviour, the weather and the number of hours of daylight. The main goal of this paper is therefore to develop an intelligent algorithm that would help optimize the trigger point to achieve the best compromise between speed reduction and power consumption. Data have been systematically collected whereby vehicle speed data were gathered whilst varying the value of the trigger speed threshold. A two stage algorithm is then utilized to extract the trigger speed value. Initially the algorithm employs a Self-Organising Map (SOM), to effectively visualize and explore the properties of the data that is then clustered in the second stage using K-means clustering method. Preliminary results achieved in the study indicate that using a SOM in conjunction with K-means method is found to perform well as opposed to direct clustering of the data by K-means alone. Using a SOM in the current case helped the algorithm determine the number of clusters in the data set, which is a frequent problem in data clustering.

  • 155.
    Jomaa, Diala
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Yella, Siril
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Edvardsson, Karin
    Dalarna University, School of Technology and Business Studies, Construction.
    Data based Calibration System for Radar used by Vehicle Activated Signs2014In: Journal of Data Analysis and Information Processing, ISSN 2327-7203, no 2, p. 11p. 106-116Article in journal (Refereed)
    Abstract [en]

    The accurate measurement of a vehicle’s velocity is an essential feature in adaptive vehicle activated sign systems. Since the velocities of the vehicles are acquired from a continuous wave Doppler radar, the data collection becomes challenging. Data accuracy is sensitive to the calibration of the radar on the road. However, clear methodologies for in-field calibration have not been carefully established. The signs are often installed by subjective judgment which results in measurement errors. This paper develops a calibration method based on mining the data collected and matching individual vehicles travelling between two radars. The data was cleaned and prepared in two ways: cleaning and reconstructing. The results showed that the proposed correction factor derived from the cleaned data corresponded well with the experimental factor done on site. In addition, this proposed factor showed superior performance to the one derived from the reconstructed data.

  • 156.
    Jusufi, Ilir
    et al.
    Department of Computer Science, University of California Davis, CA, USA.
    Nyholm, Dag
    Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Memedi, Mevludin
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Visualization of spiral drawing data of patients with Parkinson's disease2014In: IEEE International Conference on Information Visualization, IEEE Press, 2014, p. 346-350Conference paper (Refereed)
    Abstract [en]

    Patients with Parkinson's disease (PD) need to be frequently monitored in order to assess their individual symptoms and treatment-related complications. Advances in technology have introduced telemedicine for patients in remote locations. However, data produced in such settings lack much information and are not easy to analyze or interpret compared to traditional, direct contact between the patient and clinician. Therefore, there is a need to present the data using visualization techniques in order to communicate in an understandable and objective manner to the clinician. This paper presents interaction and visualization approaches used to aid clinicians in the analysis of repeated measures of spirography of PD patients gathered by means of a telemetry touch screen device. The proposed approach enables clinicians to observe fine motor impairments and identify motor fluctuations of their patients while they perform the tests from their homes using the telemetry device.

  • 157.
    KALEEM ULLAH, MUHAMMAD
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    INVENTORY CONTROL SYSTEM: Optimization of production system and reliability2010Independent thesis Advanced level (degree of Master (Two Years))Student thesis
    Abstract [en]

    The main idea of this research to solve the problem of inventory management for the paper industry SPM PVT limited. The aim of this research was to find a methodology by which the inventory of raw material could be kept at minimum level by means of buffer stock level. The main objective then lies in finding the minimum level of buffer stock according to daily consumption of raw material, finding the Economic Order Quantity (EOQ) reorders point and how much order will be placed in a year to control the shortage of raw material. In this project, we discuss continuous review model (Deterministic EOQ models) that includes the probabilistic demand directly in the formulation. According to the formula, we see the reorder point and the order up to model. The problem was tackled mathematically as well as simulation modeling was used where mathematically tractable solution was not possible. The simulation modeling was done by Awesim software for developing the simulation network. This simulation network has the ability to predict the buffer stock level based on variable consumption of raw material and lead-time. The data collection for this simulation network is taken from the industrial engineering personnel and the departmental studies of the concerned factory. At the end, we find the optimum level of order quantity, reorder point and order days.

  • 158.
    Karim, Adam
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Bypassing computer protection solutions for modern operating systems2018Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Antivirus Software Companies have seen a lot of development over the last decennary, beginning with the signature-based scanners and then slowly implementing more advanced heuristics techniques. Most of these have shown their ability to scan files stored on the hard drive and also opcodes in memory. As of date, most antivirus detection technologies used are; signature-based detection and heuristic-based detection. Malware signatures work by creating checksum hashes of known suspect files, so the smallest change prevents a match. Sometimes hackers and malicious users try finding ways around signatures and bypass signature-based detections by modifying existing malware with a few harmless strings to throw off signature based detection. I propose a project which aims to study how attackers and malicious codes can identify signatures of malware and modify it without changing its functionality with the aim of avoiding signature-based Antivirus software scanners which do not use heuristics at all. I have used netcat binary files in the reverse engineering process and proved how many antivirus scanner scan be bypassed. I also have used the python tool to create codes. From my experiment, I have proved the futility of the antivirus protection against malware. Through this report, I want to reach out and point out the safety measurements users can take to reduce attacks. I also have ensured that the common users are informed about the antivirus types and how they work. A user must be aware that antivirus software is not a full-proof protection as there are plenty of loopholes the researchers have to point out, but it does not mean the security should be compromised. Through this report, I would also like to highlight the fact that it is the responsibility of the user to follow simple guidelines to ensure protection, and the antivirus developers are responsible for keeping their database up-to-date.

  • 159.
    Khan, Imran Qayyum
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Simultaneous prediction of symptom severity and cause in data from a test battery for Parkinson patients, using machine learning methods2009Independent thesis Advanced level (degree of Master (Two Years))Student thesis
    Abstract [en]

    The main purpose of this thesis project is to prediction of symptom severity and cause in data from test battery of the Parkinson’s disease patient, which is based on data mining. The collection of the data is from test battery on a hand in computer. We use the Chi-Square method and check which variables are important and which are not important. Then we apply different data mining techniques on our normalize data and check which technique or method gives good results. The implementation of this thesis is in WEKA. We normalize our data and then apply different methods on this data. The methods which we used are Naïve Bayes, CART and KNN. We draw the Bland Altman and Spearman’s Correlation for checking the final results and prediction of data. The Bland Altman tells how the percentage of our confident level in this data is correct and Spearman’s Correlation tells us our relationship is strong. On the basis of results and analysis we see all three methods give nearly same results. But if we see our CART (J48 Decision Tree) it gives good result of under predicted and over predicted values that’s lies between -2 to +2. The correlation between the Actual and Predicted values is 0,794in CART. Cause gives the better percentage classification result then disability because it can use two classes.

  • 160.
    Khan, Imran Zafar
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Creating MPLS VPN backbone and Configuring Cisco Call Manager Express VoIP network2006Independent thesis Advanced level (degree of Master (Two Years))Student thesis
  • 161.
    Khan, Muhammad
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Hand Gesture Detection & Recognition System2012Independent thesis Advanced level (degree of Master (Two Years))Student thesis
    Abstract [en]

    The project introduces an application using computer vision for Hand gesture recognition. A camera records a live video stream, from which a snapshot is taken with the help of interface. The system is trained for each type of count hand gestures (one, two, three, four, and five) at least once. After that a test gesture is given to it and the system tries to recognize it. A research was carried out on a number of algorithms that could best differentiate a hand gesture. It was found that the diagonal sum algorithm gave the highest accuracy rate. In the preprocessing phase, a self-developed algorithm removes the background of each training gesture. After that the image is converted into a binary image and the sums of all diagonal elements of the picture are taken. This sum helps us in differentiating and classifying different hand gestures. Previous systems have used data gloves or markers for input in the system. I have no such constraints for using the system. The user can give hand gestures in view of the camera naturally. A completely robust hand gesture recognition system is still under heavy research and development; the implemented system serves as an extendible foundation for future work.

  • 162.
    KHAN, Muhammad Umair
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Use Multilevel Graph Partitioning Scheme to solve traveling salesman problem2010Independent thesis Advanced level (degree of Master (Two Years))Student thesis
    Abstract [en]

    The traveling salesman problem is although looking very simple problem but it is an important combinatorial problem. In this thesis I have tried to find the shortest distance tour in which each city is visited exactly one time and return to the starting city. I have tried to solve traveling salesman problem using multilevel graph partitioning approach. Although traveling salesman problem itself very difficult as this problem is belong to the NP-Complete problems but I have tried my best to solve this problem using multilevel graph partitioning it also belong to the NP-Complete problems. I have solved this thesis by using the k-mean partitioning algorithm which divides the problem into multiple partitions and solving each partition separately and its solution is used to improve the overall tour by applying Lin Kernighan algorithm on it. Through all this I got optimal solution which proofs that solving traveling salesman problem through graph partition scheme is good for this NP-Problem and through this we can solved this intractable problem within few minutes. Keywords: Graph Partitioning Scheme, Traveling Salesman Problem.

  • 163.
    Khan, Saqib Hussain
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods2010Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario.

    This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others.

    Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

  • 164.
    Khan, Taha
    Dalarna University, School of Technology and Business Studies, Computer Engineering. Mälardalens högskola, Akademin för innovation, design och teknik.
    First-principle data-driven models for assessment of motor disorders in Parkinson’s disease2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic. 

    The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait.

    The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

  • 165.
    Khan, Taha
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Real-Time Recognition System for Traffic Signs2008Independent thesis Advanced level (degree of Master (Two Years))Student thesis
    Abstract [en]

    The aim of this thesis project is to develop the Traffic Sign Recognition algorithm for real time. In real time environment, vehicles move at high speed on roads. For the vehicle intelligent system it becomes essential to detect, process and recognize the traffic sign which is coming in front of vehicle with high relative velocity, at the right time, so that the driver would be able to pro-act simultaneously on instructions given in the Traffic Sign. The system assists drivers about traffic signs they did not recognize before passing them. With the Traffic Sign Recognition system, the vehicle becomes aware of the traffic environment and reacts according to the situation. The objective of the project is to develop a system which can recognize the traffic signs in real time. The three target parameters are the system’s response time in real-time video streaming, the traffic sign recognition speed in still images and the recognition accuracy. The system consists of three processes; the traffic sign detection, the traffic sign recognition and the traffic sign tracking. The detection process uses physical properties of traffic signs based on a priori knowledge to detect road signs. It generates the road sign image as the input to the recognition process. The recognition process is implemented using the Pattern Matching algorithm. The system was first tested on stationary images where it showed on average 97% accuracy with the average processing time of 0.15 seconds for traffic sign recognition. This procedure was then applied to the real time video streaming. Finally the tracking of traffic signs was developed using Blob tracking which showed the average recognition accuracy to 95% in real time and improved the system’s average response time to 0.04 seconds. This project has been implemented in C-language using the Open Computer Vision Library.

  • 166.
    Khan, Taha
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering. Malardalen University, Vasteras 72123, Sweden.
    Grenholm, Peter
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Computer Vision Methods for Parkinsonian Gait Analysis: A Review on Patents2013In: Recent Patents on Biomedical Engineering, ISSN 1874-7647, Vol. 6, no 2, p. 97-108Article in journal (Refereed)
    Abstract [en]

    Gait disturbance is an important symptom of Parkinson’s disease (PD). This paper presents a review of patents reported in the area of computerized gait disorder analysis. The feasibility of marker-less vision based systems has been examined for ‘at-home’ self-evaluation of gait taking into account the physical restrictions of patients arise due to PD. A three tier review methodology has been utilized to synthesize gait applications to investigate PD related gait features and to explore methods for gait classification based on symptom severities. A comparison between invasive and non-invasive methods for gait analysis revealed that marker-free approach can provide resource efficient, convenient and accurate gait measurements through the use of image processing methods. Image segmentation of human silhouette is the major challenge in the marker-free systems which can possibly be comprehended through the use of Microsoft Kinect application and motion estimation algorithms. Our synthesis further suggests that biorhythmic features in gait patterns have potential to discriminate gait anomalies based on the clinical scales. 

  • 167.
    Khan, Taha
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Memedi, Mevludin
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Song, William Wei
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    A case study in healthcare informatics: a telemedicine framework for automated parkinson’s disease symptom assessment2014In: Smart Health: International Conference, ICSH 2014, Beijing, China, July 10-11, 2014. Proceedings / [ed] Zheng X. et al., Springer, 2014, p. 197-199Conference paper (Refereed)
    Abstract [en]

    This paper reports the development and evaluation of a mobile-based telemedicine framework for enabling remote monitoring of Parkinson’s disease (PD) symptoms. The system consists of different measurement devices for remote collection, processing and presentation of symptom data of advanced PD patients. Different numerical analysis techniques were applied on the raw symptom data to extract clinically symptom information which in turn were then used in a machine learning process to be mapped to the standard clinician-based measures. The methods for quantitative and automatic assessment of symptoms were then evaluated for their clinimetric properties such as validity, reliability and sensitivity to change. Results from several studies indicate that the methods had good metrics suggesting that they are appropriate to quantitatively and objectively assess the severity of motor impairments of PD patients.

  • 168.
    Khan, Taha
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering. Malardalen University, Vasteras 72123, Sweden.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    A Computer Vision Framework For Finger-Tapping Evaluation In Parkinson's Disease2013In: Movement Disorders: Supplement: Abstracts of the Seventeenth International Congress of Parkinson's Disease and Movement Disorders, Movement Disorder Society , 2013, p. 110-111Conference paper (Refereed)
    Abstract [en]

    Objective:

    To define and evaluate a Computer-Vision (CV) method for scoring Paced Finger-Tapping (PFT) in Parkinson's disease (PD) using quantitative motion analysis of index-fingers and to compare the obtained scores to the UPDRS (Unified Parkinson's Disease Rating Scale) finger-taps (FT).

    Background:

    The naked-eye evaluation of PFT in clinical practice results in coarse resolution to determine PD status. Besides, sensor mechanisms for PFT evaluation may cause patients discomfort. In order to avoid cost and effort of applying wearable sensors, a CV system for non-invasive PFT evaluation is introduced.

    Methods:

    A database of 221 PFT videos from 6 PD patients was processed. The subjects were instructed to position their hands above their shoulders besides the face and tap the index-finger against the thumb consistently with speed. They were facing towards a pivoted camera during recording. The videos were rated by two clinicians between symptom levels 0-to-3 using UPDRS-FT.

    The CV method incorporates a motion analyzer and a face detector. The method detects the face of testee in each video-frame. The frame is split into two images from face-rectangle center. Two regions of interest are located in each image to detect index-finger motion of left and right hands respectively. The tracking of opening and closing phases of dominant hand index-finger produces a tapping time-series. This time-series is normalized by the face height. The normalization calibrates the amplitude in tapping signal which is affected by the varying distance between camera and subject (farther the camera, lesser the amplitude). A total of 15 features were classified using K-nearest neighbor (KNN) classifier to characterize the symptoms levels in UPDRS-FT. The target ratings provided by the raters were averaged.

    Results:

    A 10-fold cross validation in KNN classified 221 videos between 3 symptom levels with 75% accuracy. An area under the receiver operating characteristic curves of 82.6% supports feasibility of the obtained features to replicate clinical assessments.

    Conclusions:

    The system is able to track index-finger motion to estimate tapping symptoms in PD. It has certain advantages compared to other technologies (e.g. magnetic sensors, accelerometers etc.) for PFT evaluation to improve and automate the ratings

  • 169.
    Khan, Taha
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering. Malardalen University, Vasteras 72123, Sweden.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    A computer vision framework for finger-tapping evaluation in Parkinson’s disease2014In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 60, no 1, p. 27-40Article in journal (Refereed)
    Abstract [en]

    Objectives: The rapid finger-tapping test (RFT) is an important method for clinical evaluation of movementdisorders, including Parkinson’s disease (PD). In clinical practice, the naked-eye evaluation of RFT results in a coarse judgment of symptom scores. We introduce a novel computer-vision (CV) method forquantification of tapping symptoms through motion analysis of index fingers. The method is unique asit utilizes facial features to calibrate tapping amplitude for normalization of distance variation betweenthe camera and subject.

    Methods: The study involved 387 video footages of RFT recorded from 13 patients diagnosed with advanced PD. Tapping performance in these videos was rated by two clinicians between the symptom severity levels (‘0: normal’ to ‘3: severe’) using the unified Parkinson’s disease rating scale motor examination of finger-tapping (UPDRS-FT). Another set of recordings in this study consisted of 84 videos of RFT recorded from 6 healthy controls. These videos were processed by a CV algorithm that tracks the index-finger motion between the video-frames to produce a tapping time-series. Different features were computed from this time series to estimate speed, amplitude, rhythm and fatigue in tapping. The features were trained in a support vector machine (1) to categorize the patient group between UPDRS-FT symptom severity levels, and (2) to discriminate between PD patients and healthy controls.

    Results: A new representative feature of tapping rhythm, ‘cross-correlation between the normalized peaks’ showed strong Guttman correlation (u2 =−0.80) with the clinical ratings. The classification oftapping features using the support vector machine classifier and 10-fold cross validation categorized the patient samples between UPDRS-FT levels with an accuracy of 88%. The same classification scheme discriminated between RFT samples of healthy controls and PD patients with an accuracy of 95%.

    Conclusion: The work supports the feasibility of the approach, which is presumed suitable for PD monitoringin the home environment. The system offers advantages over other technologies (e.g. magneticsensors, accelerometers, etc.) previously developed for objective assessment of tapping symptoms.

  • 170.
    Khan, Taha
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Assessment of PD Speech Anomalies @ Home2011In: 15th International Congress of Parkinson's Disease and Movement Disorders, Toronto, Canada, 2011Conference paper (Refereed)
    Abstract [en]

    Background: Voice processing in real-time is challenging. A drawback of previous work for Hypokinetic Dysarthria (HKD) recognition is the requirement of controlled settings in a laboratory environment. A personal digital assistant (PDA) has been developed for home assessment of PD patients. The PDA offers sound processing capabilities, which allow for developing a module for recognition and quantification HKD. Objective: To compose an algorithm for assessment of PD speech severity in the home environment based on a review synthesis. Methods: A two-tier review methodology is utilized. The first tier focuses on real-time problems in speech detection. In the second tier, acoustics features that are robust to medication changes in Levodopa-responsive patients are investigated for HKD recognition. Keywords such as Hypokinetic Dysarthria , and Speech recognition in real time were used in the search engines. IEEE explorer produced the most useful search hits as compared to Google Scholar, ELIN, EBRARY, PubMed and LIBRIS. Results: Vowel and consonant formants are the most relevant acoustic parameters to reflect PD medication changes. Since relevant speech segments (consonants and vowels) contains minority of speech energy, intelligibility can be improved by amplifying the voice signal using amplitude compression. Pause detection and peak to average power rate calculations for voice segmentation produce rich voice features in real time. Enhancements in voice segmentation can be done by inducing Zero-Crossing rate (ZCR). Consonants have high ZCR whereas vowels have low ZCR. Wavelet transform is found promising for voice analysis since it quantizes non-stationary voice signals over time-series using scale and translation parameters. In this way voice intelligibility in the waveforms can be analyzed in each time frame. Conclusions: This review evaluated HKD recognition algorithms to develop a tool for PD speech home-assessment using modern mobile technology. An algorithm that tackles realtime constraints in HKD recognition based on the review synthesis is proposed. We suggest that speech features may be further processed using wavelet transforms and used with a neural network for detection and quantification of speech anomalies related to PD. Based on this model, patients' speech can be automatically categorized according to UPDRS speech ratings.

  • 171.
    Khan, Taha
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Methods for Detection of Speech Impairment Using Mobile Devices2011In: Recent Patents on Signal Processing, ISSN 2210-6863, Vol. 1, no 2Article in journal (Refereed)
    Abstract [en]

    Speech impairment is an important symptom of Parkinson’s disease(PD). This paper presents a detailed systematic literature review on speech impairment assessment through mobile devices. A two-tier review methodology is utilized. The first tier focuses on real-time problems in speech detection. In the second tier, acoustics features that respond to medication changes in Levodopa responsive PD patients are investigated for recognition of speech symptoms. The investigation of the patents reveals that speech disorder assessment can be made by a comparative analysis between pathological acoustic patterns and the normal acoustic patterns saved in a database. The review depicts that vowel and consonant formants are the most relevant acoustic parameters to reflect PD medication changes. Since consonants have high zero-crossing rate (ZCR) whereas vowels have low ZCR, enhancements in voice segmentation can be done by inducing ZCR. Our synthesis further suggests that wavelet transforms have potential for being useful in real-time voice analysis for detection and quantification of symptoms at home.

  • 172.
    Khan, Taha
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Motion Cues Analysis for Parkinson Gait Recognition2011In: 15th International Congress of Parkinson's Disease and Movement Disorders, Toronto, Canada, 2011Conference paper (Refereed)
    Abstract [en]

    Background: Previous assessment methods for PG recognition used sensor mechanisms for PG that may cause discomfort. In order to avoid stress of applying wearable sensors, computer vision (CV) based diagnostic systems for PG recognition have been proposed. Main constraints in these methods are the laboratory setup procedures: Novel colored dresses for the patients were specifically designed to segment the test body from a specific colored background. Objective: To develop an image processing tool for home-assessment of Parkinson Gait(PG) by analyzing motion cues extracted during the gait cycles. Methods: The system is based on the idea that a normal body attains equilibrium during the gait by aligning the body posture with the axis of gravity. Due to the rigidity in muscular tone, persons with PD fail to align their bodies with the axis of gravity. The leaned posture of PD patients appears to fall forward. Whereas a normal posture exhibits a constant erect posture throughout the gait. Patients with PD walk with shortened stride angle (less than 15 degrees on average) with high variability in the stride frequency. Whereas a normal gait exhibits a constant stride frequency with an average stride angle of 45 degrees. In order to analyze PG, levodopa-responsive patients and normal controls were videotaped with several gait cycles. First, the test body is segmented in each frame of the gait video based on the pixel contrast from the background to form a silhouette. Next, the center of gravity of this silhouette is calculated. This silhouette is further skeletonized from the video frames to extract the motion cues. Two motion cues were stride frequency based on the cyclic leg motion and the lean frequency based on the angle between the leaned torso tangent and the axis of gravity. The differences in the peaks in stride and lean frequencies between PG and normal gait are calculated using Cosine Similarity measurements. Results: High cosine dissimilarity was observed in the stride and lean frequencies between PG and normal gait. High variations are found in the stride intervals of PG whereas constant stride intervals are found in the normal gait. Conclusions: We propose an algorithm as a source to eliminate laboratory constraints and discomfort during PG analysis. Installing this tool in a home computer with a webcam allows assessment of gait in the home environment.

  • 173.
    Khan, Taha
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering. Malardalen University.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Cepstral separation difference: a novel approach for speech impairment quantification in Parkinson’s disease2014In: Biocybernetics and Biomedical Engineering, ISSN 0208-5216, Vol. 34, no 1, p. 25-34Article in journal (Refereed)
    Abstract [en]

    This paper introduces a novel approach, Cepstral Separation Difference (CSD), for quantification of speech impairment in Parkinson’s disease (PD). CSD represents a ratio between the magnitudes of glottal (source) and supra-glottal (filter) log-spectrums acquired using the source-filter speech model. The CSD-based features were tested on a database consisting of 240 clinically rated running speech samples acquired from 60 PD patients and 20 healthy controls. The Guttmann (µ2) monotonic correlations between the CSD features and the speech symptom severity ratings were strong (up to 0.78). This correlation increased with the increasing textual difficulty in different speech tests. CSD was compared with some non-CSD speech features (harmonic ratio, harmonic-to-noise ratio and Mel-frequency cepstral coefficients) for speech symptom characterization in terms of consistency and reproducibility. The high intra-class correlation coefficient (>0.9) and analysis of variance indicates that CSD features can be used reliably to distinguish between severity levels of speech impairment. Results motivate the use of CSD in monitoring speech symptoms in PD.

  • 174.
    Khan, Taha
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering. Malardalen University, Vasteras 72123, Sweden.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Classification of speech intelligibility in Parkinson's disease: Speech Impairment Classification2014In: Biocybernetics and Biomedical Engineering, ISSN 0208-5216, Vol. 34, no 1, p. 35-45Article in journal (Refereed)
    Abstract [en]

    A problem in the clinical assessment of running speech in Parkinson's disease (PD) is to track underlying deficits in a number of speech components including respiration, phonation, articulation and prosody, each of which disturbs the speech intelligibility. A set of 13 features, including the cepstral separation difference and Mel-frequency cepstral coefficients were computed to represent deficits in each individual speech component. These features were then used in training a support vector machine (SVM) using n-fold cross validation. The dataset used for method development and evaluation consisted of 240 running speech samples recorded from 60 PD patients and 20 healthy controls. These speech samples were clinically rated using the Unified Parkinson's Disease Rating Scale Motor Examination of Speech (UPDRS-S). The classification accuracy of SVM was 85% in 3 levels of UPDRS-S scale and 92% in 2 levels with the average area under the ROC (receiver operating characteristic) curves of around 91%. The strong classification ability of selected features and the SVM model supports suitability of this scheme to monitor speech symptoms in PD

  • 175.
    Khan, Taha
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Motion cue analysis for parkinsonian gait recognition2013In: The open biomedical engineering journal, ISSN 1874-1207, Vol. 7, p. 1-8Article in journal (Refereed)
    Abstract [en]

    This paper presents a computer-vision based marker-free method for gait-impairment detection in Patients with Parkinson's disease (PWP). The system is based upon the idea that a normal human body attains equilibrium during the gait by aligning the body posture with Axis-of-Gravity (AOG) using feet as the base of support. In contrast, PWP appear to be falling forward as they are less-able to align their body with AOG due to rigid muscular tone. A normal gait exhibits periodic stride-cycles with stride-angle around 45o between the legs, whereas PWP walk with shortened stride-angle with high variability between the stride-cycles. In order to analyze Parkinsonian-gait (PG), subjects were videotaped with several gait-cycles. The subject's body was segmented using a color-segmentation method to form a silhouette. The silhouette was skeletonized for motion cues extraction. The motion cues analyzed were stride-cycles (based on the cyclic leg motion of skeleton) and posture lean (based on the angle between leaned torso of skeleton and AOG). Cosine similarity between an imaginary perfect gait pattern and the subject gait patterns produced 100% recognition rate of PG for 4 normal-controls and 3 PWP. Results suggested that the method is a promising tool to be used for PG assessment in home-environment.

  • 176.
    Khan, Taha
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Funk, Peter
    Mälardalen univ.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Quantification of speech impairment in Parkinson's disease2012In: Movement Disorders, ISSN 0885-3185, E-ISSN 1531-8257, Vol. 27, p. S510-S511Article in journal (Refereed)
  • 177.
    Kovàcs, Akos
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Solving the Vehicle Routing Problem with Genetic ALgorithm and Simulated Annealing2008Independent thesis Advanced level (degree of Master (Two Years))Student thesis
    Abstract [en]

    This Thesis Work will concentrate on a very interesting problem, the Vehicle Routing Problem (VRP). In this problem, customers or cities have to be visited and packages have to be transported to each of them, starting from a basis point on the map. The goal is to solve the transportation problem, to be able to deliver the packages - on time for the customers, - enough package for each Customer, - using the available resources - and – of course - to be so effective as it is possible. Although this problem seems to be very easy to solve with a small number of cities or customers, it is not. In this problem the algorithm have to face with several constraints, for example opening hours, package delivery times, truck capacities, etc. This makes this problem a so called Multi Constraint Optimization Problem (MCOP). What’s more, this problem is intractable with current amount of computational power which is available for most of us. As the number of customers grow, the calculations to be done grows exponential fast, because all constraints have to be solved for each customers and it should not be forgotten that the goal is to find a solution, what is best enough, before the time for the calculation is up. This problem is introduced in the first chapter: form its basics, the Traveling Salesman Problem, using some theoretical and mathematical background it is shown, why is it so hard to optimize this problem, and although it is so hard, and there is no best algorithm known for huge number of customers, why is it a worth to deal with it. Just think about a huge transportation company with ten thousands of trucks, millions of customers: how much money could be saved if we would know the optimal path for all our packages. Although there is no best algorithm is known for this kind of optimization problems, we are trying to give an acceptable solution for it in the second and third chapter, where two algorithms are described: the Genetic Algorithm and the Simulated Annealing. Both of them are based on obtaining the processes of nature and material science. These algorithms will hardly ever be able to find the best solution for the problem, but they are able to give a very good solution in special cases within acceptable calculation time. In these chapters (2nd and 3rd) the Genetic Algorithm and Simulated Annealing is described in details, from their basis in the “real world” through their terminology and finally the basic implementation of them. The work will put a stress on the limits of these algorithms, their advantages and disadvantages, and also the comparison of them to each other. Finally, after all of these theories are shown, a simulation will be executed on an artificial environment of the VRP, with both Simulated Annealing and Genetic Algorithm. They will both solve the same problem in the same environment and are going to be compared to each other. The environment and the implementation are also described here, so as the test results obtained. Finally the possible improvements of these algorithms are discussed, and the work will try to answer the “big” question, “Which algorithm is better?”, if this question even exists.

  • 178.
    Kripakaran, Rolance
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Face Detection and Facial Feature Localization for multi-pose faces and complex background images2011Independent thesis Advanced level (degree of Master (Two Years))Student thesis
    Abstract [en]

    The objective of this thesis work, is to propose an algorithm to detect the faces in a digital image with complex background. A lot of work has already been done in the area of face detection, but drawback of some face detection algorithms is the lack of ability to detect faces with closed eyes and open mouth. Thus facial features form an important basis for detection. The current thesis work focuses on detection of faces based on facial objects. The procedure is composed of three different phases: segmentation phase, filtering phase and localization phase. In segmentation phase, the algorithm utilizes color segmentation to isolate human skin color based on its chrominance properties. In filtering phase, Minkowski addition based object removal (Morphological operations) has been used to remove the non-skin regions. In the last phase, Image Processing and Computer Vision methods have been used to find the existence of facial components in the skin regions. This method is effective on detecting a face region with closed eyes, open mouth and a half profile face. The experiment’s results demonstrated that the detection accuracy is around 85.4% and the detection speed is faster when compared to neural network method and other techniques.

  • 179.
    Kumpulainen, Taru
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Managing ServerHotel2006Independent thesis Advanced level (degree of Master (Two Years))Student thesis
    Abstract [en]

    This graduate study was assigned by Unisys Oy Ab. The purpose of this study was to find tools to monitor and manage servers and objects in a hosting environment and to remotely connect to the managed objects. Better solutions for promised services were also researched. Unisys provides a ServerHotel service to other businesses which do not have time or resources to manage their own network, servers or applications. Contracts are based on a Service Level Agreement where service level is agreed upon according to the customer's needs. These needs have created a demand for management tools. Unisys wanted to find the most appropriate tools for its hosting environment to fulfill the agreed service level with reasonable costs. The theory consists of literary research focusing on general agreements used in the Finnish IT business, different types of monitoring and management tools and the common protocols used in them. The theory focuses mainly on the central elements of the above mentioned topics and on their positive and negative features. The second part of the study focuses on general hosting agreements and what management tools Unisys has selected for hosting and why. It also gives a more detailed account of the hosting environment and its features in more detail. Because of the results of the study Unisys decided to use Servers Alive to monitor network and MS applications’ services. Cacti was chosen to monitor disk spaces, which gives us an idea of future disk growth. For remote connections the Microsoft’s Remote Desktop tool was the most appropriate when the connection was tunneled through Secure Shell (SSH). Finding proper tools for the intended purposes with cost-conscious financial resources proved challenging. This study showed that if required, it is possible to build a professional hosting environment.

  • 180.
    Li, Yujiao
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Twitter Sentiment Analysis of New IKEA Stores Using Machine Learning2018In: 2018 International Conference on Computer and Applications, ICCA 2018, 2018, p. 4-11, article id 8460277Conference paper (Refereed)
    Abstract [en]

    This paper studied public emotion and opinion concerning the opening of new IKEA stores, specifically, how much attention are attracted, how much positive and negative emotion are aroused, what IKEA-related topics are talked due to this event. Emotion is difficult to measure in retail due to data availability and limited quantitative tools. Twitter texts, written by the public to express their opinion concerning this event, are used as a suitable data source to implement sentiment analysis. Around IKEA opening days, local people post IKEA related tweets to express their emotion and opinions on that. Such “IKEA” contained tweets are collected for opinion mining in this work. To compute sentiment polarity of tweets, lexiconbased approach is used for English tweets, and machine learning methods for Swedish tweets. The conclusion is new IKEA store are paid much attention indicated by significant increasing tweets frequency, most of them are positive emotions, and four studied cities have different topics and interests related IKEA. This paper extends knowledge of consumption emotion studies of prepurchase, provide empirical analysis of IKEA entry effect on emotion. Moreover, it develops a Swedish sentiment prediction model, elastic net method, to compute Swedish tweets’ sentiment polarity which has been rarely conducted.  

  • 181.
    Lindstrand, Martin
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Laws, Logs and Forensic Traceability: Case Study: Windows server applications (firewall, webserver, exchange server)2016Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The thesis has investigated the problem of information logging and storage from a forensic standpoint; and the study has been carried out towards a company which cannot be named for confidential reasons. what information should be logged and what has to be logged according to the Swedish law, and for how long time should the logs be saved. The problem here is to find out what in general should be saved into a log file so it can be applied to as many systems as possible. The thesis has also investigated when to do a forensic investigation and what to log in Microsoft server environment (webserver, firewall and exchange server).

    I intend to solve the problem by describing what needs to be saved in general and find out if there is a law that demands information to be logged. I intend to find out what needs to be saved from a forensic point of view. I am going to find out what should be logged on Microsoft server applications: webserver, firewall and exchange server.

    The method used to solve the problem was with practical study and literature study. Practical study where made on a Microsoft server 2012 on webserver, firewall and exchange server where I looked at the log files and what information that were saved to them.

    The report finds out what should be saved in a log file from a forensic point of view and what needs to be saved according to Swedish law. The report finds how long time the log files have to be saved. The report finds when to do a forensic investigation and what to investigate.

  • 182.
    Ma, Jiya
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    A Genetic Algorithm for Solar Boat2008Independent thesis Advanced level (degree of Master (Two Years))Student thesis
    Abstract [en]

    Genetic algorithm has been widely used in different areas of optimization problems. It has been combined with renewable energy domain, photovoltaic system, in this thesis. To participate and win the solar boat race, a control program is needed and C++ has been chosen for programming. To implement the program, the mathematic model has been built. Besides, the approaches to calculate the boundaries related to condition have been explained. Afterward, the processing of the prediction and real time control function are offered. The program has been simulated and the results proved that genetic algorithm is helpful to get the good results but it does not improve the results too much since the particularity of the solar driven boat project such as the limitation of energy production

  • 183.
    Madipally, Sunil veer Kumar
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Simulation of Sawmill Yard Operations Using Software Agents2011Independent thesis Advanced level (degree of Master (Two Years))Student thesis
    Abstract [en]

    Bergkvist insjön AB is a sawmill yard which is capable of producing 350,000 cubic meter of timber every year this requires lot of internal resources. Sawmill operations can be classified as unloading, sorting, storage and production of timber. In the company we have trucks arriving at random they have to be unloaded and sent back at the earliest to avoid queuing up of trucks creating a problem for truck owners. The sawmill yard has to operate with two log stackers that does several tasks including transporting the logs from trucks to measurement station where the logs will be sorted into classes and dropped into pockets from pockets to the sorted timber yard where they are stored and finally from there to sawmill for final processing. The main issue that needs to be answered here is the lining up trucks that are waiting to be unload, creating a problem for both sawmill as well as the truck owners and given huge production volume, it is certain that handling of resources is top priority. A key challenge in handling of resources would be unloading of trucks and finding a way to optimize internal resources. To address this problem i have experimented on different ways of using internal resources, i have designed different cases, in case 1 we have both the log stackers working on sawmill and measurement station. The main objective of having this case is to make sawmill and measurement station to work all the time. Then in case 2, i have divided the work between both the log stackers, one log stacker will be working on sawmill and pocket_control and second log stacker will be working on measurement station and truck. Then in case 3 we have only one log stacker working on all the agents, this case was designed to reduce cost of production, as the experiment cannot be done in real-time due to operational cost, for this purpose simulation is used, preliminary investigation into simulation results suggested that case 2 is the best option has it reduced waiting time of trucks considerably when compared with other cases and it showed 50% increase in optimizing internal resources.

  • 184.
    Manne, Mihira
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    MACHINE VISION FOR AUTOMATIC VISUAL INSPECTION OF WOODEN RAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS2009Independent thesis Advanced level (degree of Master (Two Years))Student thesis
    Abstract [en]

    The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection. The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT). In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features. A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers. In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted. The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification. In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.

  • 185.
    Martinsson, Robert
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Utter, Viktor
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Systemteknisk analys av LogIt Rocks: I enlighet med General Data Protection Regulation2017Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    On the 25th of May 2018, a new data protection reform; General Data ProtectionRegulation (GDPR) will take effect and replace the current legislationpersonuppgiftslagen (1998:204). The data protection directive that was issued in1995 has not been adapted to the technological advancements we use today andneeds to be renewed in order to better protect the personal privacy.In conjunction with the new data protection reform, greater demands are made oncontrollers and processors on how they treat personal data. Organizations needs toreview how they process this data and prepare themselves for the new legislation.If they fail to meet the requirements, expensive sanction fees could possibly beincurred.This work has been performed on behalf of Sogeti and LogIt Rocks ApS. Thework has however been performed in the author’s hometowns, in an environmentunbound to the employers.The report focuses on the LogIt Rocks mobile application, where an evaluation hasbeen done on whether the application is currently compatible with GDPR or not.This was made possible by interpreting the data protection reform and analyzingsystem-specific aspects of the application In order to assess if LogIt Rockscomplied with the requirements of GDPR, and propose reasonablecountermeasures where needed.The result showed that the application in many ways was not ready for GDPR.Method development, further review, anchoring and introduction of specificrecommended functions as well as specific additions proved to be necessary inorder to complete the compatibility work with GDPR.If the proposed countermeasures is ignored, heavy sanctions may be imposedunder the terms of GDPR.

  • 186.
    Mellberg Granat, Anders
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Gustavsson, Johnny
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Lösenordsstrategier på internet2017Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The purpose of this study was to investigate password strategies used by individuals creating user accounts on the internet. The study was based on a questionnaire survey, where four different target groups answered questions about their password strategies on the internet. Differences in strategies between individuals active in the field of IT/computer engineering, and individuals not active in the field, were studied. In addition, possible differences between older individuals and other target groups were investigated. Generally, the results show that there have been no major changes in password strategies over time, except that the use of password managers has increased. Strategies used by individuals active in the field of IT/computer engineering differed from those not active in the field, and it was assumed that technical interest and competence are of some importance when it comes to choosing password strategies. Among older individuals, lack of interest and unawareness was observed, leading to inadequate password strategies. The study is written in Swedish only.

  • 187.
    Memedi, Mevludin
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    A mobile-based system can assess Parkinson's disease symptoms from home environments of patients2014In: Neurologi i Sverige, ISSN 2000-8538, no 3, p. 5p. 24-28Article in journal (Other (popular science, discussion, etc.))
    Abstract [en]

    Treatment of Parkinson's disease (PD) patients involves major challenges like the large within- and between-patient variability in symptom profiles and the emergence of motor complications. As PD progresses, the symptoms develop slowly and they represent a significant source of disability in advanced patients. During evaluation of treatments and symptoms, both the physician- and patient-oriented outcomes offer complementary information. In addition, quantitative assessments of symptoms using sensing technologies can potentially complement and enhance both patient and clinician perspectives. At Högskolan Dalarna, the Lecturer Mevludin Memedi has developed a telemetry system that assesses symptoms via analysis of self-assessments and motor tests to objectively measure disease-related outcomes and to improve the management of PD.

  • 188.
    Memedi, Mevludin
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Constructive alignment in Computer Engineering and Informatics departments at Dalarna University: An empirical investigation2015Student paper otherStudent thesis
    Abstract [en]

    Background: Constructive alignment (CA) is a pedagogical approach that emphasizes the alignment between the intended learning outcomes (ILOs), teaching and learning activities (TLAs) and assessment tasks (ATs) as well as creation of a teaching/learning environment where students will be able to actively create their knowledge.

    Objectives: This paper aims at investigating the extent of constructively-aligned courses in Computer Engineering and Informatics department at Dalarna University, Sweden. This study is based on empirical observations of teacher’s perceptions of implementation of CA in their courses.

    Methods: Ten teachers (5 from each department) were asked to fill a paper-based questionnaire, which included a number of questions related to issues of implementing CA in courses.

    Results: Responses to the items of the questionnaire were mixed. Teachers clearly state the ILOs in their courses and try to align the TLAs and ATs to the ILOs. Computer Engineering teachers do not explicitly communicate the ILOs to the students as compared to Informatics teachers. In addition, Computer Engineering teachers stated that their students are less active in learning activities as compared to Informatics teachers. When asked about their subjective ratings of teaching methods all teachers stated that their current teaching is teacher-centered but they try to shift the focus of activity from them to the students.

    Conclusions: From teachers’ perspectives, the courses are partially constructively-aligned. Their courses are “aligned”, i.e. ILOs, TLAs and ATs are aligned to each other but they are not “constructive” since, according to them, there was a low student engagement in learning activities, especially in Computer Engineering department.

  • 189.
    Memedi, Mevludin
    Dalarna University, School of Technology and Business Studies, Computer Engineering. School of Science and Technology, Örebro University.
    Mobile systems for monitoring Parkinson's disease2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    A challenge for the clinical management of Parkinson's disease (PD) is the large within- and between-patient variability in symptom profiles as well as the emergence of motor complications which represent a significant source of disability in patients. This thesis deals with the development and evaluation of methods and systems for supporting the management of PD by using repeated measures, consisting of subjective assessments of symptoms and objective assessments of motor function through fine motor tests (spirography and tapping), collected by means of a telemetry touch screen device.

    One aim of the thesis was to develop methods for objective quantification and analysis of the severity of motor impairments being represented in spiral drawings and tapping results. This was accomplished by first quantifying the digitized movement data with time series analysis and then using them in data-driven modelling for automating the process of assessment of symptom severity. The objective measures were then analysed with respect to subjective assessments of motor conditions. Another aim was to develop a method for providing comparable information content as clinical rating scales by combining subjective and objective measures into composite scores, using time series analysis and data-driven methods. The scores represent six symptom dimensions and an overall test score for reflecting the global health condition of the patient. In addition, the thesis presents the development of a web-based system for providing a visual representation of symptoms over time allowing clinicians to remotely monitor the symptom profiles of their patients. The quality of the methods was assessed by reporting different metrics of validity, reliability and sensitivity to treatment interventions and natural PD progression over time.

    Results from two studies demonstrated that the methods developed for the fine motor tests had good metrics indicating that they are appropriate to quantitatively and objectively assess the severity of motor impairments of PD patients. The fine motor tests captured different symptoms; spiral drawing impairment and tapping accuracy related to dyskinesias (involuntary movements) whereas tapping speed related to bradykinesia (slowness of movements). A longitudinal data analysis indicated that the six symptom dimensions and the overall test score contained important elements of information of the clinical scales and can be used to measure effects of PD treatment interventions and disease progression. A usability evaluation of the web-based system showed that the information presented in the system was comparable to qualitative clinical observations and the system was recognized as a tool that will assist in the management of patients.

  • 190.
    Memedi, Mevludin
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Mobile systems for monitoring Parkinson's disease2011Licentiate thesis, monograph (Other academic)
    Abstract [en]

    This thesis presents the development and evaluation of IT-based methods and systems for supporting assessment of symptoms and enabling remote monitoring of Parkinson's disease (PD) patients. PD is a common neurological disorder associated with impaired body movements. Its clinical management regarding treatment outcomes and follow-up of patients is complex. In order to reveal the full extent of a patient's condition, there is a need for repeated and time-stamped assessments related to both patient's perception towards common symptoms and motor function. In this thesis, data from a mobile device test battery, collected during a three year clinical study, was used for the development and evaluation of methods. The data was gathered from a series of tests, consisting of selfassessments and motor tests (tapping and spiral drawing). These tests were carried out repeatedly in a telemedicine setting during week-long test periods. One objective was to develop a computer method that would process tracedspiral drawings and generate a score representing PD-related drawing impairments. The data processing part consisted of using the discrete wavelet transform and principal component analysis. When this computer method was evaluated against human clinical ratings, the results showed that it could perform quantitative assessments of drawing impairment in spirals comparatively well. As a part of this objective, a review of systems and methods for detecting the handwriting and drawing impairment using touch screens was performed. The review showed that measures concerning forces, accelerations, and radial displacements were the most important ones in detecting fine motor movement anomalies. Another objective of this thesis work was to design and evaluate an information system for delivering assessment support information to the treating clinical staff for monitoring PD symptoms in their patients. The system consisted of a patient node for data collection based on the mobile device test battery, a service node for data storage and processing, and a web application for data presentation. A system module was designed for compiling the test battery time series into summary scores on a test period level. The web application allowed adequate graphic feedback of the summary scores to the treating clinical staff. The evaluation results for this integrated system indicate that it can be used as a tool for frequent PD symptom assessments in home environments.

  • 191.
    Memedi, Mevludin
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Aghanavesi, Somayeh
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    A method for measuring Parkinson's disease related temporal irregularity in spiral drawings2016In: 2016 IEEE International Conference on Biomedical and Health Informatics, 2016, p. 410-413Conference paper (Refereed)
    Abstract [en]

    The objective of this paper was to develop and evaluate clinimetric properties of a method for measuring Parkinson's disease (PD)-related temporal irregularities using digital spiral analysis. In total, 108 (98 patients in different stages of PD and 10 healthy elderly subjects) performed repeated spiral drawing tasks in their home environments using a touch screen device. A score was developed for representing the amount of temporal irregularity during spiral drawing tasks, using Approximate Entropy (ApEn) technique. In addition, two previously published spiral scoring methods were adapted and their scores were analyzed. The mean temporal irregularity score differed significantly between healthy elderly subjects and advanced PD patients (P<0.005). The ApEn-based method had a better responsiveness and test-retest reliability when compared to the other two methods. In contrast to the other methods, the mean scores of the ApEn-based method improved significantly during a 3 year clinical study, indicating a possible impact of pathological basal ganglia oscillations in temporal control during spiral drawing tasks. In conclusion, the ApEn-based method could be used for differentiating between patients in different stages of PD and healthy subjects. The responsiveness and test-retest reliability were good for the ApEn-based method indicating that this method is useful for measuring upper limb temporal irregularity at a micro-level.

  • 192.
    Memedi, Mevludin
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Aghanavesi, Somayeh
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Digital spiral analysis for objective assessment of fine motor timing variability in Parkinson's disease2015Conference paper (Other academic)
    Abstract [en]

    OBJECTIVES: To develop a method for objective assessment of fine motor timing variability in Parkinson’s disease (PD) patients, using digital spiral data gathered by a touch screen device.

    BACKGROUND: A retrospective analysis was conducted on data from 105 subjects including65 patients with advanced PD (group A), 15 intermediate patients experiencing motor fluctuations (group I), 15 early stage patients (group S), and 10 healthy elderly subjects (HE) were examined. The subjects were asked to perform repeated upper limb motor tasks by tracing a pre-drawn Archimedes spiral as shown on the screen of the device. The spiral tracing test was performed using an ergonomic pen stylus, using dominant hand. The test was repeated three times per test occasion and the subjects were instructed to complete it within 10 seconds. Digital spiral data including stylus position (x-ycoordinates) and timestamps (milliseconds) were collected and used in subsequent analysis. The total number of observations with the test battery were as follows: Swedish group (n=10079), Italian I group (n=822), Italian S group (n = 811), and HE (n=299).

    METHODS: The raw spiral data were processed with three data processing methods. To quantify motor timing variability during spiral drawing tasks Approximate Entropy (APEN) method was applied on digitized spiral data. APEN is designed to capture the amount of irregularity or complexity in time series. APEN requires determination of two parameters, namely, the window size and similarity measure. In our work and after experimentation, window size was set to 4 and similarity measure to 0.2 (20% of the standard deviation of the time series). The final score obtained by APEN was normalized by total drawing completion time and used in subsequent analysis. The score generated by this method is hence on denoted APEN. In addition, two more methods were applied on digital spiral data and their scores were used in subsequent analysis. The first method was based on Digital Wavelet Transform and Principal Component Analysis and generated a score representing spiral drawing impairment. The score generated by this method is hence on denoted WAV. The second method was based on standard deviation of frequency filtered drawing velocity. The score generated by this method is hence on denoted SDDV. Linear mixed-effects (LME) models were used to evaluate mean differences of the spiral scores of the three methods across the four subject groups. Test-retest reliability of the three scores was assessed after taking mean of the three possible correlations (Spearman’s rank coefficients) between the three test trials. Internal consistency of the methods was assessed by calculating correlations between their scores.

    RESULTS: When comparing mean spiral scores between the four subject groups, the APEN scores were different between HE subjects and three patient groups (P=0.626 for S group with 9.9% mean value difference, P=0.089 for I group with 30.2%, and P=0.0019 for A group with 44.1%). However, there were no significant differences in mean scores of the other two methods, except for the WAV between the HE and A groups (P<0.001). WAV and SDDV were highly and significantly correlated to each other with a coefficient of 0.69. However, APEN was not correlated to neither WAV nor SDDV with coefficients of 0.11 and 0.12, respectively. Test-retest reliability coefficients of the three scores were as follows: APEN (0.9), WAV(0.83) and SD-DV (0.55).

    CONCLUSIONS: The results show that the digital spiral analysis-based objective APEN measure is able to significantly differentiate the healthy subjects from patients at advanced level. In contrast to the other two methods (WAV and SDDV) that are designed to quantify dyskinesias (over-medications), this method can be useful for characterizing Off symptoms in PD. The APEN was not correlated to none of the other two methods indicating that it measures a different construct of upper limb motor function in PD patients than WAV and SDDV. The APEN also had a better test-retest reliability indicating that it is more stable and consistent over time than WAV and SDDV.

  • 193.
    Memedi, Mevludin
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Aghanavesi, Somayeh
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Objective quantification of Parkinson's disease upper limb motor timing variability using spirography2015Conference paper (Refereed)
    Abstract [en]

    Objective quantification of the upper limb motor timing variability of Parkinson’s disease (PD) patients was evaluated using traces of spirals by groups of patients at different disease stages, stable (S), intermediate (I), advanced (A) and a healthy elderly (HE) group. The approximate entropy (APEN) method of quantifying motor timing variability in time series was applied to capture the amount of irregularity during the spiral drawing process. The APEN score was then normalized by total drawing completion time and used in subsequent analysis. In addition, two previously published methods (WAV and SDDV) were applied on the spiral data. Comparing subject groups’ APEN mean scores, they were found to be significantly different from HE group, for group A (P<0.001) indicating this method’s ability in distinguishing patients at advanced disease stage. Comparing the three methods’ ability to track response to advanced treatment, APEN scores were all significantly different between base-line and levodopa-carbidopa intestinal gel (LCIG) treatment, during the 36 month study period as opposed to WAV and SDDV as they were not significantly improving for all periods. APEN scores were weakly correlated to WAV and SDDV, indicating that they measure different aspects of symptom severity.

  • 194.
    Memedi, Mevludin
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Bergqvist, Ulf
    Nordforce Technology AB.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Grenholm, Peter
    Neuroscience, Neurology, Uppsala University.
    Nyholm, Dag
    Neuroscience, Neurology, Uppsala University.
    A web-based system for visualizing upper limb motor performance of Parkinson’s disease patients2013In: Movement Disorders: Supplement: Abstracts of the Seventeenth International Congress of Parkinson's Disease and Movement Disorders, Wiley-Blackwell, 2013, p. S112-S113Conference paper (Refereed)
    Abstract [en]

    Objective

    To design, develop and set up a web-based system for enabling graphical visualization of upper limb motor performance (ULMP) of Parkinson’s disease (PD) patients to clinicians.

    Background

    Sixty-five patients diagnosed with advanced PD have used a test battery, implemented in a touch-screen handheld computer, in their home environment settings over the course of a 3-year clinical study. The test items consisted of objective measures of ULMP through a set of upper limb motor tests (finger to tapping and spiral drawings). For the tapping tests, patients were asked to perform alternate tapping of two buttons as fast and accurate as possible, first using the right hand and then the left hand. The test duration was 20 seconds. For the spiral drawing test, patients traced a pre-drawn Archimedes spiral using the dominant hand, and the test was repeated 3 times per test occasion. In total, the study database consisted of symptom assessments during 10079 test occasions.

    Methods

    Visualization of ULMP

    The web-based system is used by two neurologists for assessing the performance of PD patients during motor tests collected over the course of the said study. The system employs animations, scatter plots and time series graphs to visualize the ULMP of patients to the neurologists. The performance during spiral tests is depicted by animating the three spiral drawings, allowing the neurologists to observe real-time accelerations or hesitations and sharp changes during the actual drawing process. The tapping performance is visualized by displaying different types of graphs. Information presented included distribution of taps over the two buttons, horizontal tap distance vs. time, vertical tap distance vs. time, and tapping reaction time over the test length.

    Assessments

    Different scales are utilized by the neurologists to assess the observed impairments. For the spiral drawing performance, the neurologists rated firstly the ‘impairment’ using a 0 (no impairment) – 10 (extremely severe) scale, secondly three kinematic properties: ‘drawing speed’, ‘irregularity’ and ‘hesitation’ using a 0 (normal) – 4 (extremely severe) scale, and thirdly the probable ‘cause’ for the said impairment using 3 choices including Tremor, Bradykinesia/Rigidity and Dyskinesia. For the tapping performance, a 0 (normal) – 4 (extremely severe) scale is used for first rating four tapping properties: ‘tapping speed’, ‘accuracy’, ‘fatigue’, ‘arrhythmia’, and then the ‘global tapping severity’ (GTS). To achieve a common basis for assessment, initially one neurologist (DN) performed preliminary ratings by browsing through the database to collect and rate at least 20 samples of each GTS level and at least 33 samples of each ‘cause’ category. These preliminary ratings were then observed by the two neurologists (DN and PG) to be used as templates for rating of tests afterwards. In another track, the system randomly selected one test occasion per patient and visualized its items, that is tapping and spiral drawings, to the two neurologists.

    Statistical methods

    Inter-rater agreements were assessed using weighted Kappa coefficient. The internal consistency of properties of tapping and spiral drawing tests were assessed using Cronbach’s α test. One-way ANOVA test followed by Tukey multiple comparisons test was used to test if mean scores of properties of tapping and spiral drawing tests were different among GTS and ‘cause’ categories, respectively.

    Results

    When rating tapping graphs, inter-rater agreements (Kappa) were as follows: GTS (0.61), ‘tapping speed’ (0.89), ‘accuracy’ (0.66), ‘fatigue’ (0.57) and ‘arrhythmia’ (0.33). The poor inter-rater agreement when assessing “arrhythmia” may be as a result of observation of different things in the graphs, among the two raters. When rating animated spirals, both raters had very good agreement when assessing severity of spiral drawings, that is, ‘impairment’ (0.85) and irregularity (0.72). However, there were poor agreements between the two raters when assessing ‘cause’ (0.38) and time-information properties like ‘drawing speed’ (0.25) and ‘hesitation’ (0.21). Tapping properties, that is ‘tapping speed’, ‘accuracy’, ‘fatigue’ and ‘arrhythmia’ had satisfactory internal consistency with a Cronbach’s α coefficient of 0.77. In general, the trends of mean scores of tapping properties worsened with increasing levels of GTS. The mean scores of the four properties were significantly different to each other, only at different levels. In contrast from tapping properties, kinematic properties of spirals, that is ‘drawing speed’, ‘irregularity’ and ‘hesitation’ had a questionable consistency among them with a coefficient of 0.66. Bradykinetic spirals were associated with more impaired speed (mean = 83.7 % worse, P < 0.001) and hesitation (mean = 77.8% worse, P < 0.001), compared to dyskinetic spirals. Both these ‘cause’ categories had similar mean scores of ‘impairment’ and ‘irregularity’.

    Conclusions

    In contrast from current approaches used in clinical setting for the assessment of PD symptoms, this system enables clinicians to animate easily and realistically the ULMP of patients who at the same time are at their homes. Dynamic access of visualized motor tests may also be useful when observing and evaluating therapy-related complications such as under- and over-medications. In future, we foresee to utilize these manual ratings for developing and validating computer methods for automating the process of assessing ULMP of PD patients.

  • 195.
    Memedi, Mevludin
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Jusufi, Ilir
    Computer Science, University of California, Davis, USA.
    Nyholm, Dag
    Uppsala University, Neuroscience, Neurology.
    Visualization of spirography-based objective measures in Parkinson's disease2014In: Movement Disorders Supplement: Abstracts of the Eighteenth International Congress of Parkinson's Disease and Movement Disorders, Wiley-Blackwell, 2014, p. S187-S189Conference paper (Other academic)
    Abstract [en]

    Objective: To investigate whether advanced visualizations of spirography-based objective measures are useful in differentiating drug-related motor dysfunctions between Off and dyskinesia in Parkinson’s disease (PD).

    Background: During the course of a 3 year longitudinal clinical study, in total 65 patients (43 males and 22 females with mean age of 65) with advanced PD and 10 healthy elderly (HE) subjects (5 males and 5 females with mean age of 61) were assessed. Both patients and HE subjects performed repeated and time-stamped assessments of their objective health indicators using a test battery implemented on a telemetry touch screen handheld computer, in their home environment settings. Among other tasks, the subjects were asked to trace a pre-drawn Archimedes spiral using the dominant hand and repeat the test three times per test occasion.

    Methods: A web-based framework was developed to enable a visual exploration of relevant spirography-based kinematic features by clinicians so they can in turn evaluate the motor states of the patients i.e. Off and dyskinesia. The system uses different visualization techniques such as time series plots, animation, and interaction and organizes them into different views to aid clinicians in measuring spatial and time-dependent irregularities that could be associated with the motor states. Along with the animation view, the system displays two time series plots for representing drawing speed (blue line) and displacement from ideal trajectory (orange line). The views are coordinated and linked i.e. user interactions in one of the views will be reflected in other views. For instance, when the user points in one of the pixels in the spiral view, the circle size of the underlying pixel increases and a vertical line appears in the time series views to depict the corresponding position. In addition, in order to enable clinicians to observe erratic movements more clearly and thus improve the detection of irregularities, the system displays a color-map which gives an idea of the longevity of the spirography task. Figure 2 shows single randomly selected spirals drawn by a: A) patient who experienced dyskinesias, B) HE subject, and C) patient in Off state.

    Results: According to a domain expert (DN), the spirals drawn in the Off and dyskinesia motor states are characterized by different spatial and time features. For instance, the spiral shown in Fig. 2A was drawn by a patient who showed symptoms of dyskinesia; the drawing speed was relatively high (cf. blue-colored time series plot and the short timestamp scale in the x axis) and the spatial displacement was high (cf. orange-colored time series plot) associated with smooth deviations as a result of uncontrollable movements. The patient also exhibited low amount of hesitation which could be reflected both in the animation of the spiral as well as time series plots. In contrast, the patient who was in the Off state exhibited different kinematic features, as shown in Fig. 2C. In the case of spirals drawn by a HE subject, there was a great precision during the drawing process as well as unchanging levels of time-dependent features over the test trial, as seen in Fig. 2B.

    Conclusions: Visualizing spirography-based objective measures enables identification of trends and patterns of drug-related motor dysfunctions at the patient’s individual level. Dynamic access of visualized motor tests may be useful during the evaluation of drug-related complications such as under- and over-medications, providing decision support to clinicians during evaluation of treatment effects as well as improve the quality of life of patients and their caregivers. In future, we plan to evaluate the proposed approach by assessing within- and between-clinician variability in ratings in order to determine its actual usefulness and then use these ratings as target outcomes in supervised machine learning, similarly as it was previously done in the study performed by Memedi et al. (2013).

  • 196.
    Memedi, Mevludin
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Khan, Taha
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Grenholm, Peter
    Department of Neuroscience, Neurology, Uppsala University.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Automatic and objective assessment of alternating tapping performance in Parkinson’s disease2013In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 13, no 12, p. 16965-16984Article in journal (Refereed)
    Abstract [en]

    This paper presents the development and evaluation of a method for enabling quantitative and automatic scoring of alternating tapping performance of patients with Parkinson’s disease (PD). Ten healthy elderly subjects and 95 patients in different clinical stages of PD have utilized a touch-pad handheld computer to perform alternate tapping tests in their home environments. First, a neurologist used a web-based system to visually assess impairments in four tapping dimensions (‘speed’, ‘accuracy’, ‘fatigue’ and ‘arrhythmia’) and a global tapping severity (GTS). Second, tapping signals were processed with time series analysis and statistical methods to derive 24 quantitative parameters. Third, principal component analysis was used to reduce the dimensions of these parameters and to obtain scores for the four dimensions. Finally, a logistic regression classifier was trained using a 10-fold stratified cross-validation to map the reduced parameters to the corresponding visually assessed GTS scores. Results showed that the computed scores correlated well to visually assessed scores and were significantly different across Unified Parkinson’s Disease Rating Scale scores of upper limb motor performance. In addition, they had good internal consistency, had good ability to discriminate between healthy elderly and patients in different disease stages, had good sensitivity to treatment interventions and could reflect the natural disease progression over time. In conclusion, the automatic method can be useful to objectively assess the tapping performance of PD patients and can be included in telemedicine tools for remote monitoring of tapping.

  • 197.
    Memedi, Mevludin
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Nyholm, Dag
    Neuroscience, Neurology, Uppsala University.
    Computerized identification of motor complications in Parkinson's disease2014In: Movement Disorders Supplement: Abstracts of the Eighteenth International Congress of Parkinson's Disease and Movement Disorders, 2014, p. S187-S188Conference paper (Other academic)
    Abstract [en]

    Objective: To investigate whether spirography-based objective measures are able to effectively characterize the severity of unwanted symptom states (Off and dyskinesia) and discriminate them from motor state of healthy elderly subjects.

    Background: Sixty-five patients with advanced Parkinson’s disease (PD) and 10 healthy elderly (HE) subjects performed repeated assessments of spirography, using a touch screen telemetry device in their home environments. On inclusion, the patients were either treated with levodopa-carbidopa intestinal gel or were candidates for switching to this treatment. On each test occasion, the subjects were asked trace a pre-drawn Archimedes spiral shown on the screen, using an ergonomic pen stylus. The test was repeated three times and was performed using dominant hand. A clinician used a web interface which animated the spiral drawings, allowing him to observe different kinematic features, like accelerations and spatial changes, during the drawing process and to rate different motor impairments. Initially, the motor impairments of drawing speed, irregularity and hesitation were rated on a 0 (normal) to 4 (extremely severe) scales followed by marking the momentary motor state of the patient into 2 categories that is Off and Dyskinesia. A sample of spirals drawn by HE subjects was randomly selected and used in subsequent analysis.

    Methods: The raw spiral data, consisting of stylus position and timestamp, were processed using time series analysis techniques like discrete wavelet transform, approximate entropy and dynamic time warping in order to extract 13 quantitative measures for representing meaningful motor impairment information. A principal component analysis (PCA) was used to reduce the dimensions of the quantitative measures into 4 principal components (PC). In order to classify the motor states into 3 categories that is Off, HE and dyskinesia, a logistic regression model was used as a classifier to map the 4 PCs to the corresponding clinically assigned motor state categories. A stratified 10-fold cross-validation (also known as rotation estimation) was applied to assess the generalization ability of the logistic regression classifier to future independent data sets. To investigate mean differences of the 4 PCs across the three categories, a one-way ANOVA test followed by Tukey multiple comparisons was used.

    Results: The agreements between computed and clinician ratings were very good with a weighted area under the receiver operating characteristic curve (AUC) coefficient of 0.91. The mean PC scores were different across the three motor state categories, only at different levels. The first 2 PCs were good at discriminating between the motor states whereas the PC3 was good at discriminating between HE subjects and PD patients. The mean scores of PC4 showed a trend across the three states but without significant differences. The Spearman’s rank correlations between the first 2 PCs and clinically assessed motor impairments were as follows: drawing speed (PC1, 0.34; PC2, 0.83), irregularity (PC1, 0.17; PC2, 0.17), and hesitation (PC1, 0.27; PC2, 0.77).

    Conclusions: These findings suggest that spirography-based objective measures are valid measures of spatial- and time-dependent deficits and can be used to distinguish drug-related motor dysfunctions between Off and dyskinesia in PD. These measures can be potentially useful during clinical evaluation of individualized drug-related complications such as over- and under-medications thus maximizing the amount of time the patients spend in the On state.

  • 198.
    Memedi, Mevludin
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering. School of Science and Technology, Örebro University.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University.
    Johansson, Anders
    Department of Clinical Neuroscience, Neurology, Karolinska Institutet.
    Pålhagen, Sven
    Department of Clinical Neuroscience, Neurology, Karolinska Institutet.
    Willows, Thomas
    Department of Neurology, Karolinska University Hospital.
    Widner, Håkan
    Department of Neurology, Skåne University Hospital.
    Linder, Jan
    Department of Pharmacology and Clinical Neuroscience, Umeå University.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Self-assessments and motor tests via telemetry in a 36-month levodopa-carbidopa intestinal gel infusion trial2014Manuscript (preprint) (Other academic)
    Abstract [en]

    Objective: The aim of this study was to investigate if a telemetry test battery can be used to measure effects of Parkinson’s disease (PD) treatment intervention and disease progression.

    Methods: Sixty-five patients diagnosed with advanced PD were recruited in an open longitudinal 36-month study; 35 treated with levodopa-carbidopa intestinal gel (LCIG) and 30 were candidates for switching from oral PD treatment to LCIG. They utilized a test battery, consisting of self-assessments of symptoms and fine motor tests (tapping and spiral drawings), four times per day in their homes during week-long test periods. The repeated measurements were summarized into an overall test score (OTS) to represent the global condition of the patient during a test period. Clinical assessments included ratings on Unified PD Rating Scale (UPDRS) and 39-item PD Questionnaire (PDQ-39) scales.

    Results: In LCIG-naïve patients, mean OTS compared to baseline was significantly improved from the first test period on LCIG treatment until month 24. In LCIG-non-naïve patients, there were no significant changes in mean OTS, except at month 36 (P<0.01). The OTS correlated adequately with total UPDRS (rho = 0.59) and total PDQ-39 (0.59).

    Conclusions: PD symptoms can be remotely monitored over time with this test battery. The trends of the test scores were similar to the trends of clinical rating scores. Correlations between OTS and clinical rating scales were adequate indicating that the test battery contains important elements of the information of the well-established scales.

  • 199.
    Memedi, Mevludin
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Johansson, Anders
    Department of Clinical Neuroscience, Neurology, Karolinska Institutet, Stockholm, Sweden.
    Pålhagen, Sven
    Department of Neurology, Karolinska University Hospital, Stockholm, Sweden.
    Willows, Thomas
    Department of Neurology, Karolinska University Hospital, Stockholm, Sweden.
    Widner, Håkan
    Department of Neurology, Skåne University Hospital, Lund, Sweden.
    Linder, Jan
    Department of Pharmacology and Clinical Neuroscience, Umeå University, Umeå, Sweden.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Validity and responsiveness of at-home touch-screen assessments in advanced Parkinson's disease2015In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 19, no 6, p. 1829-1834Article in journal (Refereed)
    Abstract [en]

    The aim of this study was to investigate if a telemetry test battery can be used to measure effects of Parkinson’s disease (PD) treatment intervention and disease progression in patients with fluctuations. Sixty-five patients diagnosed with advanced PD were recruited in an open longitudinal 36-month study; 35 treated with levodopa-carbidopa intestinal gel (LCIG) and 30 were candidates for switching from oral PD treatment to LCIG. They utilized a test battery, consisting of self-assessments of symptoms and fine motor tests (tapping and spiral drawings), four times per day in their homes during week-long test periods. The repeated measurements were summarized into an overall test score (OTS) to represent the global condition of the patient during a test period. Clinical assessments included ratings on Unified PD Rating Scale (UPDRS) and 39-item PD Questionnaire (PDQ-39) scales. In LCIG-naïve patients, mean OTS compared to baseline was significantly improved from the first test period on LCIG treatment until month 24. In LCIG-non-naïve patients, there were no significant changes in mean OTS until month 36. The OTS correlated adequately with total UPDRS (rho = 0.59) and total PDQ-39 (0.59). Responsiveness measured as effect size was 0.696 and 0.536 for OTS and UPDRS respectively. The trends of the test scores were similar to the trends of clinical rating scores but dropout rate was high. Correlations between OTS and clinical rating scales were adequate indicating that the test battery contains important elements of the information of well-established scales. The responsiveness and reproducibility were better for OTS than for total UPDRS.

  • 200.
    Memedi, Mevludin
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.