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  • 1.
    Aghanavesi, Somayeh
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Smartphone-based Parkinson’s disease symptom assessment2017Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis consists of four research papers presenting a microdata analysis approach to assess and evaluate the Parkinson’s disease (PD) motor symptoms using smartphone-based systems. PD is a progressive neurological disorder that is characterized by motor symptoms. It is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Both patients’ perception regarding common symptom and their motor function need to be related to the repeated and time-stamped assessment; with this, the full extent of patient’s condition could be revealed. The smartphone enables and facilitates the remote, long-term and repeated assessment of PD symptoms. Two types of collected data from smartphone were used, one during a three year, and another during one-day clinical study. The data were collected from series of tests consisting of tapping and spiral motor tests. During the second time scale data collection, along smartphone-based measurements patients were video recorded while performing standardized motor tasks according to Unified Parkinson’s disease rating scales (UPDRS).

    At first, the objective of this thesis was to elaborate the state of the art, sensor systems, and measures that were used to detect, assess and quantify the four cardinal and dyskinetic motor symptoms. This was done through a review study. The review showed that smartphones as the new generation of sensing devices are preferred since they are considered as part of patients’ daily accessories, they are available and they include high-resolution activity data. Smartphones can capture important measures such as forces, acceleration and radial displacements that are useful for assessing PD motor symptoms.

    Through the obtained insights from the review study, the second objective of this thesis was to investigate whether a combination of tapping and spiral drawing tests could be useful to quantify dexterity in PD. More specifically, the aim was to develop data-driven methods to quantify and characterize dexterity in PD. The results from this study showed that tapping and spiral drawing tests that were collected by smartphone can detect movements reasonably well related to under- and over-medication.

    The thesis continued by developing an Approximate Entropy (ApEn)-based method, which aimed to measure the amount of temporal irregularity during spiral drawing tests. One of the disabilities associated with PD is the impaired ability to accurately time movements. The increase in timing variability among patients when compared to healthy subjects, suggests that the Basal Ganglia (BG) has a role in interval timing. ApEn method was used to measure temporal irregularity score (TIS) which could significantly differentiate the healthy subjects and patients at different stages of the disease. This method was compared to two other methods which were used to measure the overall drawing impairment and shakiness. TIS had better reliability and responsiveness compared to the other methods. However, in contrast to other methods, the mean scores of the ApEn-based method improved significantly during a 3-year clinical study, indicating a possible impact of pathological BG oscillations in temporal control during spiral drawing tasks. In addition, due to the data collection scheme, the study was limited to have no gold standard for validating the TIS. However, the study continued to further investigate the findings using another screen resolution, new dataset, new patient groups, and for shorter term measurements. The new dataset included the clinical assessments of patients while they performed tests according to UPDRS. The results of this study confirmed the findings in the previous study. Further investigation when assessing the correlation of TIS to clinical ratings showed the amount of temporal irregularity present in the spiral drawing cannot be detected during clinical assessment since TIS is an upper limb high frequency-based measure. 

  • 2.
    Aghanavesi, Somayeh
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    A review of Parkinson’s disease cardinal and dyskinetic motor symptoms assessment methods using sensor systems2016Conference paper (Refereed)
    Abstract [en]

    This paper is reviewing objective assessments of Parkinson’s disease(PD) motor symptoms, cardinal, and dyskinesia, using sensor systems. It surveys the manifestation of PD symptoms, sensors that were used for their detection, types of signals (measures) as well as their signal processing (data analysis) methods. A summary of this review’s finding is represented in a table including devices (sensors), measures and methods that were used in each reviewed motor symptom assessment study. In the gathered studies among sensors, accelerometers and touch screen devices are the most widely used to detect PD symptoms and among symptoms, bradykinesia and tremor were found to be mostly evaluated. In general, machine learning methods are potentially promising for this. PD is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Combining existing technologies to develop new sensor platforms may assist in assessing the overall symptom profile more accurately to develop useful tools towards supporting better treatment process.

  • 3.
    Butt, Abdul Haleem
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Speech Assessment for the Classification of Hypokinetic Dysthria in Parkinson Disease2012Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The aim of this thesis is to investigate computerized voice assessment methods to classify between the normal and Dysarthric speech signals. In this proposed system, computerized assessment methods equipped with signal processing and artificial intelligence techniques have been introduced. The sentences used for the measurement of inter-stress intervals (ISI) were read by each subject. These sentences were computed for comparisons between normal and impaired voice. Band pass filter has been used for the preprocessing of speech samples. Speech segmentation is performed using signal energy and spectral centroid to separate voiced and unvoiced areas in speech signal. Acoustic features are extracted from the LPC model and speech segments from each audio signal to find the anomalies. The speech features which have been assessed for classification are Energy Entropy, Zero crossing rate (ZCR), Spectral-Centroid, Mean Fundamental-Frequency (Meanf0), Jitter (RAP), Jitter (PPQ), and Shimmer (APQ). Naïve Bayes (NB) has been used for speech classification. For speech test-1 and test-2, 72% and 80% accuracies of classification between healthy and impaired speech samples have been achieved respectively using the NB. For speech test-3, 64% correct classification is achieved using the NB. The results direct the possibility of speech impairment classification in PD patients based on the clinical rating scale.

  • 4. Davami, Erfan
    et al.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Classification with NormalBoost2011In: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 20, no 2, p. 187-208Article in journal (Refereed)
    Abstract [en]

    This paper presents a new boosting algorithm called NormalBoost which is capable of classifying a multi-dimensional binary class dataset. It adaptively combines several weak classifiers to form a strong classifier. Unlike many boosting algorithms which have high computation and memory complexities, NormalBoost is capable of classification with low complexity. Since NormalBoost assumes the dataset to be continuous, it is also noise resistant because it only deals with the means and standard deviations of each dimension. Experiments conducted to evaluate its performance shows that NormalBoost performs almost the same as AdaBoost in the classification rate. However, NormalBoost performs 189 times faster than AdaBoost and employs a very little amount of memory when a dataset of 2 million samples with 50 dimensions is invoked.

  • 5.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Segmentation and enhancement of low quality fingerprint images2016In: Web Information Systems Engineering – WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part II, China - Shanghai: Springer, 2016, Vol. 10042, p. 371-384Conference paper (Refereed)
    Abstract [en]

    This paper presents a new approach to segment low quality finger-print images which are collected by low quality fingerprint scanners. Images collected using such readers are easy to collect but difficult to segment. The proposed approach focuses on automatically segment and enhance these fingerprint images to reduce the detection of false minutiae and hence improve the recognition rate. There are four major contributions of this paper. Firstly, segmentation of fingerprint images is achieved via morphological filters to find the largest object in the image which is the foreground of the fingerprint. Secondly, specially designed adaptive thresholding algorithm to deal with fingerprint images. The algorithm tries to fit a curve between the gray levels of the pixels of each row or column in the fingerprint image. The curve represents the binarization threshold of each pixel in the corresponding row or column. Thirdly, noise reduction and ridge enhancement is achieved by invoking a rotational invariant anisotropic diffusion filter. Finally, an adaptive thinning algorithm which is immune against spurs is invoked to generate the recognition ready fingerprint image. Segmentation of 100 images from databases FVC2002 and FVC2004 was performed and the experiments showed that 96 % of images under test are correctly segmented.

  • 6.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Traffic Sign detection and recognition2017In: Computer Vision and Imaging in Intelligent Transportation Systems, John Wiley & Sons, 2017, 1, p. 343-374Chapter in book (Refereed)
    Abstract [en]

    This chapter presents an overview of traffic sign detection and recognition. It describes the characteristics of traffic signs and the requirements and difficulties when dealing with traffic sign detection and recognition in outdoor images. The chapter also covers the different techniques invoked to segment traffic signs from the different traffic scenes and the techniques employed for the recognition and classification of traffic signs. It points many problems regarding the stability of the received colour information, variations of these colours with respect to the daylight conditions, and absence of a colour model that can led to a good solution. It also proposes an adaptive colour segmentation model based on Neural Networks. The chapter demonstrates the way to classify segmented traffic signs by employing one of widely used classifiers, AdaBoost , based on a set of features, in this case HOG descriptors, which was developed for pedestrian recognition but found the way for many applications in different fields. The chapter ends by showing examples where traffic sign recognition is applicable in vehicle industry

  • 7.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Traffic sign recognition without color information2015Report (Other academic)
    Abstract [en]

    Color represents an important attribute in the field of traffic sign recognition. However, when the color of the traffic sign fades or the traffic scene is collected in gray as in the case of Infrared imaging, then color based recognition systems fail. Other problems related to color are simply that different countries use different colors. Even within the European Union, colors of traffic signs are not the same.

    This paper aims to present a new approach to detect traffic signs without color attributes. It is based a two-stage sliding window which detects traffic signs in the multi-scale image. Histogram of Oriented Gradients (HOG) descriptors are computed as a quality function which are evaluated by two SVM classifier; the coarse and the fine detectors. 

    Different objects detected by the coarse detectors are clustered and a fine search is conducted in the areas where traffic signs are more probable to exist. 

    Experiments conducted to detect traffic signs under different light conditions such as sunny, cloudy, fog and snow fall have showed a performance of 98% and very low false positive rate.  The proposed approach was tested on the Yield traffic signs because it has a simple triangular shape which can be found in many places other than the traffic signs and represent a challenge to the proposed approach.

  • 8.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Barsam, Payvar
    Optimization of cable cycles: a trade-off between reliability and cost2015In: American Journal of Intelligent Systems, ISSN 2165-8978, E-ISSN 2165-8994, Vol. 5, no 2, p. 43-57Article in journal (Refereed)
    Abstract [en]

    This paper elaborates the routing of cable cycle through available routes in a building in order to link a set of devices, in a most reasonable way. Despite of the similarities to other NP-hard routing problems, the only goal is not only to minimize the cost (length of the cycle) but also to increase the reliability of the path (in case of a cable cut) which is assessed by a risk factor. Since there is often a trade-off between the risk and length factors, a criterion for ranking candidates and deciding the most reasonable solution is defined. A set of techniques is proposed to perform an efficient and exact search among candidates. A novel graph is introduced to reduce the search-space, and navigate the search toward feasible and desirable solutions. Moreover, admissible heuristic length estimation helps to early detection of partial cycles which lead to unreasonable solutions. The results show that the method provides solutions which are both technically and financially reasonable. Furthermore, it is proved that the proposed techniques are very efficient in reducing the computational time of the search to a reasonable amount.

  • 9.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Bhuiyan, Nizam
    Biswas, Rubel
    Prohibitory traffic signs detection using LVQ and windowed Hough transform2011In: IICAI-11 (5 th Indian International Conference on Artificial Intelligence), Tumkur, India, 2011Conference paper (Refereed)
    Abstract [en]

    Prohibitory traffic signs represent an important group of traffic signs which are used to prohibit certain types of manoeuvres or some types of traffic. Speed limits signs belong to this group and speed is the main cause of many deadly accidents. Detecting this group in good time may be helpful to avoid many fatal accidents. This paper presents a new approach to detecting prohibitory traffic signs which is based on colour segmentation using LVQ and windowed Hough Transform. Experiments conducted to check the robustness of this approach indicated that 98.5% of the traffic signs invoked for this test were successfully detected. This test was carried out using images collected under a wide range of environmental conditions.

  • 10.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Bin Mumtaz, Al Hasanat
    Adaptive Shadow and Highlight Invariant Colour Segmentation for Traffic Sign Recognition Based on Kohonen SOM2011In: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 20, no 1, p. 15-31Article in journal (Refereed)
    Abstract [en]

    This paper describes an intelligent algorithm for traffic sign recognition which converges quickly, is accurate in its segmentation and adaptive in its behaviour. The proposed approach can segment images of traffic signs in different lighting and environmental conditions and in different countries. It is based on using Kohonen's Self-Organizing Maps (SOM) as a clustering tool and it is developed for Intelligent Vehicle applications. The current approach does not need any prior training. Instead, a slight portion, which is about 1% of the image under investigation, is used for training. This is a key issue to ensure fast convergence and high adaptability. The current approach was tested by using 442 images which were collected under different environmental conditions and from different countries. The proposed approach shows promising results; good improvement of 73% is observed in faded traffic sign images compared with 53.3% using the traditional algorithm. The adaptability of the system is evident from the segmentation of the traffic sign images from various countries where the result is 96% for the nine countries included in the test.

  • 11.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Biswas, Rubel
    Davami, Erfan
    Traffic sign detection based on AdaBoost color segmentation and SVM classification2013In: Eurocon 2013: IEEE Conference Publications / [ed] IEEE, 2013, p. 2005-2010Conference paper (Refereed)
    Abstract [en]

    This paper aims to present a new approach to detect traffic signs which is based on color segmentation using AdaBoost binary classifier and circular Hough Transform.The Adaboost classifier was trained to segment traffic signs images according to the desired color. A voting mechanism was invoked to establish a property curve for each of the candidates. SVM classifier was trained to classify the property curves of each object into their corresponding classes.

    Experiments conducted on Adaboost color segmentation under different light conditions such as sunny, cloudy, fog and snow fall have showed a performance of 95%. The proposed system was tested on two different groups of traffic signs; the warning and the prohibitory signs. In the case of warning signs, a recognition rate of 98.4% was achieved while it was 97% for prohibitory traffic signs. This test was carried out under a wide range of environmental conditions.

  • 12.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Davami, Erfan
    Eigen Based Traffic Sign Recognition Which Aids In Achieving Intelligent Speed Adaptation2011In: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 20, no 2, p. 129-145Article in journal (Refereed)
    Abstract [en]

    Speed is one of the major factors by which the traffic safety is affected. If the speed limit traffic signs on the road are recognised and displayed to a driver, this will be a motivation to keep the vehicle's speed within the permitted range. The purpose of this paper is to investigate Eigen-based traffic sign recognition which can aid in the development of Intelligent Speed Adaptation. This system is based on invoking the PCA technique to detect the unknown speed limit traffic sign and computes its best effective Eigen vectors. The traffic sign is then recognized and classified by using the shortest Euclidean distance to the different speed limit traffic sign classes. The system was trained using 24 037 images which were collected in different light conditions. To check the robustness of this system, it was tested against 1429 images and it was found that the accuracy of recognition was 97.5% which indicates clearly the high robustness targeted by this system.

  • 13.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Davami, Erfan
    University of Central Florida.
    Multiclass Adaboost Based on an Ensemble of Binary Adaboosts2013In: American Journal of Intelligent Systems, ISSN 2165-8978, E-ISSN 2165-8994, Vol. 3, no 2, p. 57-70Article in journal (Refereed)
    Abstract [en]

    This paper presents a multi-class AdaBoost based on incorporating an ensemble of binary AdaBoosts which is organized as Binary Decision Tree (BDT). It is proved that binary AdaBoost is extremely successful in producing accurate classification but it does not perform very well for multi-class problems. To avoid this performance degradation, the multi-class problem is divided into a number of binary problems and binary AdaBoost classifiers are invoked to solve these classification problems. This approach is tested with a dataset consisting of 6500 binary images of traffic signs. Haar-like features of these images are computed and the multi-class AdaBoost classifier is invoked to classify them. A classification rate of 96.7% and 95.7% is achieved for the traffic sign boarders and pictograms, respectively. The proposed approach is also evaluated using a number of standard datasets such as Iris, Wine, Yeast, etc. The performance of the proposed BDT classifier is quite high as compared with the state of the art and it converges very fast to a solution which indicates it as a reliable classifier.

  • 14.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Jomaa, Diala
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Davami, Erfan
    Segmentation of fingerprint images based on bi-level combination of global and local processing2012In: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 21, no 2, p. 97-120Article in journal (Refereed)
    Abstract [en]

    This paper presents a new approach to segment low quality fingerprint imageswhich are collected by low quality fingerprint readers. Images collected using such readersare easy to collect but difficult to segment. The proposed approach is based on combiningglobal and local processing to achieve segmentation of fingerprint images. On the globallevel, the fingerprint is located and extracted from the rest of the image by using a globalthresholding followed by dilation and edge detection of the largest object in the image.On the local level, fingerprint’s foreground and its border image are treated using differentfuzzy rules. These rules are based on the mean and variance of the block under consideration.The approach is implemented in three stages: pre-processing, segmentation, andpost-processing.Segmentation of 100 images was performed and compared with manual examinationsby human experts. The experiments showed that 96% of images under test are correctlysegmented. The results from the quality of segmentation test revealed that the averageerror in block segmentation was 2.84% and the false positive and false negatives wereapproximately 1.4%. This indicates the high robustness of the proposed approach.

  • 15.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Mohammed, Iman
    Night time vehicle detection2012In: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 21, no 2, p. 143-165Article in journal (Refereed)
    Abstract [en]

    Night driving is one of the major factors which affects traffic safety. Althoughdetecting oncoming vehicles at night time is a challenging task, it may improve trafficsafety. If the oncoming vehicle is recognised in good time, this will motivate drivers tokeep their eyes on the road. The purpose of this paper is to present an approach to detectvehicles at night based on the employment of a single onboard camera. This system isbased on detecting vehicle headlights by recognising their shapes via an SVM classifierwhich was trained for this purpose. A pairing algorithm was designed to pair vehicleheadlights to ensure that the two headlights belong to the same vehicle. A multi-objecttracking algorithm was invoked to track the vehicle throughout the time the vehicle isin the scene. The system was trained with 503 single objects and tested using 144 587single objects which were extracted from 1410 frames collected from 15 videos and 27moving vehicles. It was found that the accuracy of recognition was 97.9% and the vehiclerecognition rate was 96.3% which indicates clearly the high robustness attained by thissystem.

  • 16.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Roch, Janina
    TU Kaiserslautern, Kaiserslautern, Germany.
    Benchmark Evaluation of HOG Descriptors as Features for Classification of Traffic Signs2013Report (Other academic)
    Abstract [en]

    The purpose of this paper is to analyze the performance of the Histograms of Oriented Gradients (HOG) as descriptors for traffic signs recognition. The test dataset consists of speed limit traffic signs because of their high inter-class similarities.

      HOG features of speed limit signs, which were extracted from different traffic scenes, were computed and a Gentle AdaBoost classifier was invoked to evaluate the different features. The performance of HOG was tested with a dataset consisting of 1727 Swedish speed signs images. Different numbers of HOG features per descriptor, ranging from 36 features up 396 features, were computed for each traffic sign in the benchmark testing. The results show that HOG features perform high classification rate as the Gentle AdaBoost classification rate was 99.42%, and they are suitable to real time traffic sign recognition. However, it is found that changing the number of orientation bins has insignificant effect on the classification rate. In addition to this, HOG descriptors are not robust with respect to sign orientation.

  • 17.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Yella, Siril
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Hansson, Karl
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Feature selection and bleach time modelling of paper pulp using tree based learners2016In: Web Information Systems Engineering – WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part I / [ed] Wojciech CellaryMohamed F. MokbelJianmin WangHua WangRui ZhouYanchun Zhang, China - Shanghai: Springer, 2016, Vol. 10042, p. 385-396Conference paper (Refereed)
    Abstract [en]

    Paper manufacturing is energy demanding and improvedmodelling of the pulp bleach process is the main non-invasive means ofreducing energy costs. In this paper, time it takes to bleach paper pulpto desired brightness is examined. The model currently used is analysedand benchmarked against two machine learning models (Random Forestand TreeBoost). Results suggests that the current model can be super-seded by the machine learning models and it does not use the optimalcompact subset of features. Despite the differences between the machinelearning models, a feature ranking correlation has been observed for thenew models. One novel, yet unused, feature that both machine learningmodels found to be important is the concentration of bleach agent.

  • 18.
    Gasso, Edwini
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A Comparison Between Structural Equations and Predictive Modelling Approaches for Estimation of Causal Effects for Hospital Outlier on Patient Outcomes: The Case of Patient Care Units in Dalarna Region, Sweden2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Many studies have been done on the identification of the causal effect of a treatment (or intervention) but still the field of causal effect identification is highly debated in many disciplines e.g. data science, econometrics, biostatistics etc. This study examined the theoretical motivation of predictive modelling approach to estimate and check the sensitivity analysis for causal effects by using structural equations modelling framework with application of real data from Landstinget Dalarna. In this study, the link between causal inference using structural equations modelling approach of Heckman's two-step estimation framework and predictive modeling approach has been established. Furthermore, two different simulation studies under linearity and nonlinearity assumptions were conducted to see the finite sample properties of the predictive modelling approaches such as Support Vector Machine, Random Forest, Gaussian boosted regression trees and Bayesian additive regression trees. Finally, the predictive modelling approaches were used to estimate the treatment effects on patient outcomes in terms of length of stay, unplanned readmission and mortality within 30 days after discharge. The results were also compared with the traditional approaches: propensity score matching, propensity weighting and two-step estimation method. This study used two types of estimates, average treatment effects and treatment on treated. The estimates and their standard errors were calculated for the real data from Landstinget Dalarna. The study found that, non-outlying patients are staying in the hospital for longer periods of time (in days) compared to outlying patients though the reasons that make long of stay remained unknown. For readmission and mortality, the results varied a lot between the alternative models, and we can therefore not conclude that non-outlying patients have higher mortality and readmission rates compared to outlying.

  • 19.
    Jayaram, M.A.
    et al.
    Siddaganga Institute of Technology.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Convex Hulls in Image Processing: A Scoping Review2016In: American Journal of Intelligent Systems, ISSN 2165-8978, E-ISSN 2165-8994, Vol. 6, no 2, p. 48-58Article in journal (Refereed)
    Abstract [en]

    The demands of image processing related systems are robustness, high recognition rates, capability to handle incomplete digital information, and magnanimous flexibility in capturing shape of an object in an image. It is exactly here that, the role of convex hulls comes to play. The objective of this paper is twofold. First, we summarize the state of the art in computational convex hull development for researchers interested in using convex hull image processing to build their intuition, or generate nontrivial models. Secondly, we present several applications involving convex hulls in image processing related tasks. By this, we have striven to show researchers the rich and varied set of applications they can contribute to. This paper also makes a humble effort to enthuse prospective researchers in this area. We hope that the resulting awareness will result in new advances for specific image recognition applications.

  • 20.
    Jayaram, M.A.
    et al.
    Siddaganga Institute of Technology, Tumakuru, Karnataka, India.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Whither Edge Computing? – A Futuristic Review2018In: International Journal of Applied Research on Information Technology and Computing, ISSN 0975-8070, Vol. 9, no 2, p. 180-188Article in journal (Refereed)
    Abstract [en]

    It is a well-known fact that the current day Internet is increasingly becoming laden with content that is bandwidth demanding due to ever-increasing number of things getting attached on a day-in and day-out basis. Hand-in-hand, mobile networks and data networks are converging into cloud computing bandwagon. Edge computing as a promising feature has already made inroads to face future requirements and to address exponential demands from cloud. This feature is all about inserting computing power and storage in the vicinity of the network edge. It is asserted that this scheme of operation brings down the data transport time, quick response times and increased availability. Edge computing brings bandwidthintensive content and latency-sensitive applications closer to the user or data source. In this paper, we explain the drivers of edge computing and have delved on various types of edge computing currently available and going to throng in near future. This paper is intended to draw a comprehensive picture of what is happening in edge currently and what would happen in the near foreseeable future.

  • 21.
    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.

  • 22.
    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.

  • 23.
    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.

  • 24.
    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).

  • 25.
    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.

  • 26.
    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.

  • 27.
    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.

  • 28.
    Memedi, Mevludin
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Nyholm, Dag
    Neuroscience, Neurology, Uppsala University.
    Johansson, Anders
    Clinical Neuroscience, Karolinska Institutet.
    Pålhagen, Sven
    Clinical Neuroscience, Karolinska Institutet.
    Willows, Thomas
    Neurology, Karolinska University Hospital.
    Widner, Håkan
    Neurology, Skåne University Hospital.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Self-reported symptoms and motor tests via telemetry in a 36-month levodopa-carbidopa intestinal gel infusion trial2013In: Movement Disorders :  Supplement: Abstracts of the Seventeenth International Congress of Parkinson's Disease and Movement Disorders S1, Wiley-Blackwell, 2013, p. S168-S168Conference paper (Refereed)
    Abstract [en]

    Objective

    To investigate if a home environment test battery can be used to measure effects of Parkinson’s disease (PD) treatment intervention and disease progression.

    Background

    Seventy-seven patients diagnosed with advanced PD were recruited in an open longitudinal 36-month study at 10 clinics in Sweden and Norway; 40 of them were treated with levodopa-carbidopa intestinal gel (LCIG) and 37 patients were candidates for switching from oral PD treatment to LCIG. They utilized a mobile device test battery, consisting of self-assessments of symptoms and objective measures of motor function through a set of fine motor tests (tapping and spiral drawings), in their homes. Both the LCIG-naïve and LCIG-non-naïve patients used the test battery four times per day during week-long test periods.

    Methods

    Assessments

    The LCIG-naïve patients used the test battery at baseline (before LCIG), month 0 (first visit; at least 3 months after intraduodenal LCIG), and thereafter quarterly for the first year and biannually for the second and third years. The LCIG-non-naïve patients used the test battery from the first visit, i.e. month 0. Out of the 77 patients, only 65 utilized the test battery; 35 were LCIG-non-naïve and 30 LCIG-naïve. In 20 of the LCIG-naïve patients, assessments with the test battery were available during oral treatment and at least one test period after having started infusion treatment. Three LCIG-naïve patients did not use the test battery at baseline but had at least one test period of assessments thereafter. Hence, n=23 in the LCIG-naïve group. In total, symptom assessments in the full sample (including both patient groups) were collected during 379 test periods and 10079 test occasions. For 369 of these test periods, clinical assessments including UPDRS and PDQ-39 were performed in afternoons at the start of the test periods. The repeated measurements of the test battery were processed and summarized into scores representing patients’ symptom severities over a test period, using statistical methods. Six conceptual dimensions were defined; four subjectively-reported: ‘walking’, ‘satisfied’, ‘dyskinesia’, and ‘off’ and two objectively-measured: ‘tapping’ and ‘spiral’. In addition, an ‘overall test score’ (OTS) was defined to represent the global health condition of the patient during a test period.

    Statistical methods

    Change in the test battery scores over time, that is at baseline and follow-up test periods, was assessed with linear mixed-effects models with patient ID as a random effect and test period as a fixed effect of interest. The within-patient variability of OTS was assessed using intra-class correlation coefficient (ICC), for the two patient groups. Correlations between clinical rating scores and test battery scores were assessed using Spearman’s rank correlations (rho).

    Results

    In LCIG-naïve patients, mean OTS compared to baseline was significantly improved from the first test period on LCIG treatment until month 24. However, there were no significant changes in mean OTS scores of LCIG-non-naïve patients, except for worse mean OTS at month 36 (p<0.01, n=16). The mean scores of all subjectively-reported dimensions improved significantly throughout the course of the study, except ‘walking’ at month 36 (p=0.41, n=4). However, there were no significant differences in mean scores of objectively-measured dimensions between baseline and other test periods, except improved ‘tapping’ at month 6 and month 36, and ‘spiral’ at month 3 (p<0.05). The LCIG-naïve patients had a higher within-subject variability in their OTS scores (ICC=0.67) compared to LCIG-non-naïve patients (ICC=0.71). The OTS correlated adequately with total UPDRS (rho=0.59) and total PDQ-39 (rho=0.59).

    Conclusions

    In this 3-year follow-up study of advanced PD patients treated with LCIG we found that it is possible to monitor PD progression over time using a home environment test battery. The significant improvements in the mean OTS scores indicate that the test battery is able to measure functional improvement with LCIG sustained over at least 24 months.

  • 29.
    Memedi, Mevludin
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering. Örebro University.
    Nyholm, Dag
    Neuroscience, Neurology, Uppsala University.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Combined fine-motor tests and self-assessments for remote detection of motor fluctuations2013In: Recent Patents on Biomedial Engineering, ISSN 1874-7647, Vol. 6, no 2, p. 127-135Article in journal (Refereed)
    Abstract [en]

    A major problem with the clinical management of fluctuating movement disorders, e.g. Parkinson's disease (PD), is the large variability in manifestation of symptoms among patients. In this condition, frequent measurements which account for both patient-reported and objective assessments are needed in order to capture symptom fluctuations, with the purpose to optimize therapy. The main focus of this paper is to present a mobile-based system for enabling remote monitoring of PD patients from their home environment conditions. The system consists of a patient diary section for collecting patient-based self-assessments, a motor test section for collecting fine motor movements through upper limb motor tests, and a scheduler for restricting operation to a multitude of predetermined limited time intervals. The system processes and compiles time series data into different summary scores representing symptom severity. In addition, the paper presents a review of recent inventions which were filed after year 2000 in the field of telemedicine applications. The review includes a summary of systems and methods which enable remote symptom assessments of patients, not necessarily suffering from movement disorders, through repeated measurements and which take into account their subjective and/or objective health indicators. The findings conclude that there are a small number of inventions which collect subjective and objective health measures in telemedicine settings. Consequently, there is a lack of mechanisms that combine these two types of information into scores to provide a more in-depth assessment of the patient's general health, their motor and non-motor symptom fluctuations and treatment effects. The paper also provides a discussion concerning different approaches for analyzing and combining subjective and objective measures, and handling data from longitudinal studies.

  • 30.
    Ogeskär, Tobias
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Forensisk analys av volatilt minne från operativsystemet OS X2014Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The need to analyze volatile memory on Macintosh computers with OS X has become increasingly important due to the fact that their computers have become more popular and volatile memory analysis has become a more important part of an IT-forensics work. The reason volatile memory analysis has become more important is that it's possible to find information that’s not stored permanently on the computer’s hard drive. The problem that formed the basis for this thesis was that it was obvious there was a lack of methods of investigation of the volatile memory for Macs running OS X.The aim of this work was therefore to investigate the possibility of extracting information from a volatile memory from a Mac computer with OS X by identifying and assessing different methods of investigation. To do this investigation, literature studies, informal interviews, own knowledge and practical attempts have been conducted.It was concluded that the ability to extract information from the volatile memory from a Mac-computer with OS X is relatively limited. The biggest problem is the dumping of the memory. Many of the available dumping methods require administrative rights. When analyzing a memory dump you should never rely on one analyze method since different analyze methods give different results that can be useful for further investigation of a Mac-computer.

  • 31.
    Paidi, Vijay
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Nyberg, Roger G.
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Smart parking sensors, technologies and applications for open parking lots: a review2018In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 12, no 8, p. 735-741Article in journal (Refereed)
    Abstract [en]

    Parking a vehicle in traffic dense environments often leads to excess time of driving in search for free space which leads to congestions and environmental pollution. Lack of guidance information to vacant parking spaces is one reason for inefficient parking behaviour. Smart parking sensors and technologies facilitate guidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensors or technologies is in use for open parking lot. This paper reviews the literature on the usage of smart parking sensors, technologies, applications and evaluate their applicability to open parking lots. Magnetometers, ultrasonic sensors and machine vision were few of the widely used sensors and technologies on closed parking lots. However, this paper suggests a combination of machine vision, convolutional neural network or multi-agent systems suitable for open parking lots due to less expenditure and resistance to varied environmental conditions. Few smart parking applications show drivers the location of common open parking lots. No application provided real time parking occupancy information, which is a necessity to guide them along the shortest route to free space. To develop smart parking applications for open parking lots, further research is needed in the fields of deep learning and multi-agent systems.

  • 32.
    Paidi, Vijay
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Nyberg, Roger G.
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Smart Parking Tools Suitability for Open Parking Lots: A Review2018In: Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems, Madeira, 2018, p. 600-609Conference paper (Refereed)
    Abstract [en]

    Parking a vehicle in traffic dense environments is a common issue in many parts of the world which oftenleads to congestion and environmental pollution. Lack of guidance information to vacant parking spaces isone of the reasons for inefficient parking behaviour. Smart parking sensors and technologies facilitateguidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensorsor technologies are in use for the common open parking lot. This paper reviews the literature on the usage ofsmart parking sensors, technologies, applications and evaluate their suitability to open parking lots. Suitabilitywas made in terms of expenditure and reliability. Magnetometers, ultrasonic sensors and machine vision werefew of the widely used sensors and technologies used in closed parking lots. However, this paper suggests acombination of machine vision, fuzzy logic or multi-agent systems suitable for open parking lots due to lessexpenditure and resistance to varied environmental conditions. No application provided real time parkingoccupancy information of open parking lots, which is a necessity to guide them along the shortest route tofree space. To develop smart parking applications for open parking lots, further research is needed in the fieldsof deep learning.

  • 33.
    Rebreyend, Pascal
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Lemarchand, Laurent
    Massé, Damien
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Multiobjective Optimization for Multimode Transportation Problems2018In: Advances in Operations Research, ISSN 1687-9147, E-ISSN 1687-9155, article id 8720643Article in journal (Refereed)
    Abstract [en]

    We propose modelling for a facilities localization problem in the context of multimode transportation. The applicative goal is to locate service facilities such as schools or hospitals while optimizing the different transportation modes to these facilities. We formalize the School Problem and solve it first exactly using an adapted -constraint multiobjective method. Because of the size of the instances considered, we have also explored the use of heuristic methods based on evolutionary multiobjective frameworks, namely, NSGA2 and a modified version of PAES. Those methods are mixed with an original local search technique to provide better results. Numerical comparisons of solutions sets quality are made using the hypervolume metric. Based on the results for test-cases that can be solved exactly, efficient implementation for PAES and NSGA2 allows execution times comparison for large instances. Results show good performances for the heuristic approaches as compared to the exact algorithm for small test-cases. Approximate methods present a scalable behavior on largest problem instances. A master/slave parallelization scheme also helps to reduce execution times significantly for the modified PAES approach.

  • 34.
    Sadikov, Aleksander
    et al.
    University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, Slovenia.
    Groznik, Vida
    University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, Slovenia.
    Možina, Martin
    University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, Slovenia.
    Žabkar, Jure
    University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, Slovenia.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Memedi, Mevludin
    Dalarna University, School of Technology and Business Studies, Computer Engineering. Informatics, School of Business, Örebro University, Örebro, Sweden.
    Georgiev, Dejan
    Ljubljana University Medical Centre, Department of Neurology, Zaloška 2, Ljubljana, Slovenia.
    Feasibility of spirography features for objective assessment of motor function in Parkinson's disease2017In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 81, no SI, p. 54-62Article in journal (Refereed)
    Abstract [en]

    Objective

    Parkinson's disease (PD) is currently incurable, however proper treatment can ease the symptoms and significantly improve the quality of life of patients. Since PD is a chronic disease, its efficient monitoring and management is very important. The objective of this paper was to investigate the feasibility of using the features and methodology of a spirography application, originally designed to detect early Parkinson's disease (PD) motoric symptoms, for automatically assessing motor symptoms of advanced PD patients experiencing motor fluctuations. More specifically, the aim was to objectively assess motor symptoms related to bradykinesias (slowness of movements occurring as a result of under-medication) and dyskinesias (involuntary movements occurring as a result of over-medication).

    Materials and methods

    This work combined spirography data and clinical assessments from a longitudinal clinical study in Sweden with the features and pre-processing methodology of a Slovenian spirography application. The study involved 65 advanced PD patients and over 30,000 spiral-drawing measurements over the course of three years. Machine learning methods were used to learn to predict the “cause” (bradykinesia or dyskinesia) of upper limb motor dysfunctions as assessed by a clinician who observed animated spirals in a web interface. The classification model was also tested for comprehensibility. For this purpose a visualisation technique was used to present visual clues to clinicians as to which parts of the spiral drawing (or its animation) are important for the given classification.

    Results

    Using the machine learning methods with feature descriptions and pre-processing from the Slovenian application resulted in 86% classification accuracy and over 0.90 AUC. The clinicians also rated the computer's visual explanations of its classifications as at least meaningful if not necessarily helpful in over 90% of the cases.

    Conclusions

    The relatively high classication accuracy and AUC demonstrates the usefulness of this approach for objective monitoring of PD patients. The positive evaluation of computer's explanations suggests the potential use of this methodology in a decision support setting.

  • 35.
    Thomas, Ilias
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Automating levodopa dosing schedules for Parkinson’s disease2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Parkinson’s disease (PD) is the second most common neurodegenerative disease. Levodopa is mainly used to manage the motor symptoms of PD. However, disease progression and long-term use of levodopa cause reduced medication efficacy and side effects. When that happens, precise individualized dosing schedules are required.

    This doctoral thesis in the field of Micro-data analysis introduces an end-to-end solution for the automation of the pharmacological management of PD with levodopa, and offers some insight on levodopa pharmacodynamics. For that purpose, an algorithm that derives objective ratings for the patients’ motor function through wearable sensors is introduced, a method to construct individual patient profiles is developed, and two dosing algorithms for oral and intestinal administration of levodopa are presented. Data from five different sources were used to develop the methods and evaluate the performance of the proposed algorithms.

    The dose automation algorithms can work both with clinical and objective ratings (through wearable devices), and their application was evaluated against dosing adjustments from movement disorders experts. Both dosing algorithms showed promise and their dosing suggestions were similar to those of the clinicians.

    The objective ratings algorithm had good test-retest reliability and its application during a clinical study was successful. Furthermore, the method of fitting individual patient models was robust and worked well with the objective ratings algorithm. Finally, a study was carried out that showed that about half the patients on levodopa treatment show reduced response during the afternoon hours, pointing to the need for more precise modelling of levodopa pharmacodynamics.

  • 36.
    Thomas, Ilias
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Optimizing levodopa dosing routines for Parkinson’s disease2017Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis in the field of microdata analysis aims to introduce dose optimizing algorithms for the pharmacological management of Parkinson’s disease (PD). PD is a neurodegenerative disease that mostly affects the motor functions of the patients and it is characterized as a movement disorder. The core symptoms of PD are: bradykinesia, postural instability, rigidity, and tremor. There is no cure for PD and the use of levodopa to manage the core symptoms is considered the gold standard. However, long term use of levodopa causes reduced medication efficacy, and side effects, such as dyskinesia, which can also be attributed to overmedication. When that happens precise individualized dosing schedules are required. The goal of this thesis is to examine if algorithmic methods can be used to find dosing schedules that treat PD symptoms and minimize manifestation of side effects. Data from three different sources were used for that purpose: data from a clinical study in Uppsala University hospital in 2015, patient admission chart data from Uppsala University hospital during 2011-2015, and data from a clinical study in Gothenburg University during 2016-2017. The data were used to develop the methods and evaluate the performance of the proposed algorithms.The first algorithm that was developed was a sensor-based method that derives objective measurements (ratings) of PD motor states. The construction of the sensor index was based on subjective ratings of patients’ motor functions made by three movement disorder experts. This sensor-based method was used when deriving algorithmic dosing schedules. Afterwards, a method that uses medication information and ratings of the patients’ motor states to fit individual patient models was developed. This method uses mathematical optimization to individualize specific parameters of dose-effects models for levodopa intake, through minimizing the distance between motor state ratings and dose-effect curves. Finally, two different dose optimization algorithms were developed and evaluated, that had as input the individual patient models. The first algorithm was specific to continuous infusion of levodopa treatment, where the patient’s state was set to a specific target value and the algorithm made dosing adjustments to keep that patients motor functions on that state. The second algorithm concerned oral administration of microtables of levodopa. The ambition with this algorithm was that the suggested doses would find the right balance between treating the core symptoms of PD and, at the same time, minimizing the side effects of long term levodopa use, mainly dyskinesia. Motor state ratings for this study were obtained through the sensor index. Both algorithms followed a principle of deriving a morning dose and a maintenance dose for the patients, with maintenance dose being an infusion rate for the first algorithm, and oral administration doses at specific time points for the second algorithm.The results showed that the sensor-based index had good test-retest reliability, sensitivity to levodopa treatment, and ability to make predictions in unseen parts of the dataset. The dosing algorithm for continuous infusion of levodopa had a good ability to suggest an optimal infusion rating for the patients, but consistently suggested lower morning dose than what the treating personnel prescribed. The dosing algorithm for oral administration of levodopa showed great agreement with the treating personnel’s prescriptions, both in terms of morning and maintenance dose. Moreover, when evaluating the oral medication algorithm, it was clear that the sensor index ratings could be used for building patient specific models.

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