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  • 1.
    Dougherty, Mark
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Westin, Jerker
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Slutrapport för projektet E-MOTIONS2013Rapport (Övrigt vetenskapligt)
  • 2.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    SVM based traffic sign classification using legender moments2007Ingår i: Proceedings of the 3rd Indian International Conference on Artificial Intelligence, IICAI 2007, 2007, s. 957-968Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a novel approach to recognize traffic signs using Support Vector Machines (SVMs) and Legendre Moments. Images of traffic signs are collected by a digital camera mounted in a vehicle. They are color segmented and all objects which represent signs are extracted and normalized to 36×36 pixels images. Legendre moments of sign borders and speed-limit signs of 350 and 250 images are computed and the SVM classifier is trained with theses features. Two stages of SVM are trained; the first stage determines the class of the sign from the shape of its border and the second one determines the pictogram of the sign. Training and testing of both SVM classifiers are done offline by using still images. In the online mode, the system loads the SVM training model and performs recognition. Copyright © 2007 IICAI.

  • 3.
    Hedlund, Niklas
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    IT­-Forensisk undersökning av flyktigt minne: På Linux och Android enheter2013Självständigt arbete på grundnivå (kandidatexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [sv]

    Att kunna gör en effektiv undersökning av det flyktiga minnet är något som blir viktigare ochviktigare i IT-forensiska utredningar. Dels under Linux och Windows baserade PC installationermen också för mobila enheter i form av Android och enheter baserade andra mobila opperativsy-stem.Android använder sig av en modifierad Linux-kärna var modifikationer är för att anpassa kärnantill de speciella krav som gäller för ett mobilt operativsystem. Dessa modifikationer innefattardels meddelandehantering mellan processer men även ändringar till hur internminnet hanteras ochövervakas.Då dessa två kärnor är så pass nära besläktade kan samma grundläggande principer användas föratt dumpa och undersöka minne. Dumpningen sker via en kärn-modul vilket i den här rapportenutgörs av en programvara vid namn LiME vilken kan hantera bägge kärnorna.Analys av minnet kräver att verktygen som används har en förståelse för minneslayouten i fråga.Beroende på vilken metod verktyget använder så kan det även behövas information om olika sym-boler. Verktyget som används i det här examensarbetet heter Volatility och klarar på papperet avatt extrahera all den information som behövs för att kunna göra en korrekt undersökning.Arbetet avsåg att vidareutveckla existerande metoder för analys av det flyktiga minnet på Linux-baserade maskiner (PC) och inbyggda system(Android). Problem uppstod då undersökning avflyktigt minne på Android och satta mål kunde inte uppnås fullt ut. Det visade sig att minnesanalysriktat emot PC-plattformen är både enklare och smidigare än vad det är mot Android.

  • 4.
    Jomaa, Diala
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Triggering Radar Speed Warning signs using Association Rules and Clustering Techniques2012Konferensbidrag (Refereegranskat)
    Abstract [en]

    Radar speed warning signs (RSWS) have been used in recent years across Sweden and elsewhere in the world. Such signs measure vehicle speed using radar and are designed to display a message when the driver exceeds a pre-set threshold speed, which is often relative to the speed limit on a particular road segment. RSWS are typically placed on locations which are perceived to be problematic by relevant authorities. Excessive speeding or road accidents are examples of such perceived problems.  Deploying RSWS in many relevant locations is often impractical due to the lack of necessary power supply needed for operation. Battery driven RSWS are an alternative but are less attractive because of limited running time and frequent maintenance (changing batteries etc). Therefore, solar powered RSWS are more desirable. However, these signs are also dependent on batteries that need to be charged. The duration of operation of solar powered RSWS largely depend on how often the sign is triggered. Constant activation of the sign drains the battery. It is desirable to trigger the sign only when necessary. Hence, the main goal of this research is to design a model that optimises the performance of RSWS depending on prevailing conditions i.e traffic flows during different times of the day and so on.  Vehicle speed data had been collected at a test site in Sweden all hours of the day. This paper attempts to use a hybrid system based on Apriori and K-means clustering algorithm. Apriori algorithm is simple and efficient to determine associations’ rules among attributes in particular to discover the most common combination that can occur within the data set.  K-means clustering is basically used to quantize the input variables into smaller clusters that can easily derive the trigger threshold value. The proposed hybrid system indicated that the system was able to trigger solar RWWS efficiently.

  • 5.
    Jomaa, Diala
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Speed prediction for triggering vehicle activated signs2016Rapport (Övrigt vetenskapligt)
    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.

  • 6.
    Jusufi, Ilir
    et al.
    Department of Computer Science, University of California Davis, CA, USA.
    Nyholm, Dag
    Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Memedi, Mevludin
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Visualization of spiral drawing data of patients with Parkinson's disease2014Ingår i: IEEE International Conference on Information Visualization, IEEE Press, 2014, s. 346-350Konferensbidrag (Refereegranskat)
    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.

  • 7.
    Khan, Taha
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Memedi, Mevludin
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Song, William Wei
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
    Westin, Jerker
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    A case study in healthcare informatics: a telemedicine framework for automated parkinson’s disease symptom assessment2014Ingår i: Smart Health: International Conference, ICSH 2014, Beijing, China, July 10-11, 2014. Proceedings / [ed] Zheng X. et al., Springer, 2014, s. 197-199Konferensbidrag (Refereegranskat)
    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.

  • 8.
    Khan, Taha
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Westin, Jerker
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Motion cue analysis for parkinsonian gait recognition2013Ingår i: The open biomedical engineering journal, ISSN 1874-1207, Vol. 7, s. 1-8Artikel i tidskrift (Refereegranskat)
    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.

  • 9.
    Memedi, Mevludin
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    A mobile-based system can assess Parkinson's disease symptoms from home environments of patients2014Ingår i: Neurologi i Sverige, ISSN 2000-8538, nr 3, s. 5s. 24-28Artikel i tidskrift (Övrig (populärvetenskap, debatt, mm))
    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.

  • 10.
    Memedi, Mevludin
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Aghanavesi, Somayeh
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Westin, Jerker
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Digital spiral analysis for objective assessment of fine motor timing variability in Parkinson's disease2015Konferensbidrag (Övrigt vetenskapligt)
    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.

  • 11.
    Memedi, Mevludin
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Aghanavesi, Somayeh
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Westin, Jerker
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Objective quantification of Parkinson's disease upper limb motor timing variability using spirography2015Konferensbidrag (Refereegranskat)
    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.

  • 12.
    Memedi, Mevludin
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    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
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Validity and responsiveness of at-home touch-screen assessments in advanced Parkinson's disease2015Ingår i: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 19, nr 6, s. 1829-1834Artikel i tidskrift (Refereegranskat)
    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.

  • 13.
    Memedi, Mevludin
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Sadikov, Aleksander
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Groznik, Vida
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Žabkar, Jure
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Možina, Martin
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Bergquist, Filip
    Sahlgrenska Academy, Department of Pharmacology, University of Gothenburg, Gothenburg, Sweden.
    Johansson, Anders
    Neurology, Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
    Haubenberger, Dietrich
    NINDS Intramural Research Program, Clinical Trials Unit, National Institutes of Health, Bethesda, MD, USA.
    Nyholm, Dag
    Neurology, Neuroscience, Uppsala University, Uppsala, Sweden.
    Automatic spiral analysis for objective assessment of motor symptoms in Parkinson's disease2015Ingår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 15, nr 9, s. 23727-23744Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A challenge for the clinical management of advanced Parkinson’s disease (PD) patients is the emergence of fluctuations in motor performance, which represents a significant source of disability during activities of daily living of the patients. There is a lack of objective measurement of treatment effects for in-clinic and at-home use that can provide an overview of the treatment response. The objective of this paper was to develop a method for objective quantification of advanced PD motor symptoms related to off episodes and peak dose dyskinesia, using spiral data gathered by a touch screen telemetry device. More specifically, the aim was to objectively characterize motor symptoms (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Digitized upper limb movement data of 65 advanced PD patients and 10 healthy (HE) subjects were recorded as they performed spiral drawing tasks on a touch screen device in their home environment settings. Several spatiotemporal features were extracted from the time series and used as inputs to machine learning methods. The methods were validated against ratings on animated spirals scored by four movement disorder specialists who visually assessed a set of kinematic features and the motor symptom. The ability of the method to discriminate between PD patients and HE subjects and the test-retest reliability of the computed scores were also evaluated. Computed scores correlated well with mean visual ratings of individual kinematic features. The best performing classifier (Multilayer Perceptron) classified the motor symptom (bradykinesia or dyskinesia) with an accuracy of 84% and area under the receiver operating characteristics curve of 0.86 in relation to visual classifications of the raters. In addition, the method provided high discriminating power when distinguishing between PD patients and HE subjects as well as had good test-retest reliability. This study demonstrated the potential of using digital spiral analysis for objective quantification of PD-specific and/or treatment-induced motor symptoms.

  • 14.
    Memedi, Mevludin
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Sadikov, Aleksander
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Groznik, Vida
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Žabkar, Jure
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Možina, Martin
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Bergquist, Filip
    Sahlgrenska Academy, Department of Pharmacology, University of Gothenburg, Gothenburg, Sweden.
    Johansson, Anders
    Neurology, Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
    Haubenberger, Dietrich
    NINDS Intramural Research Program, Clinical Trials Unit, National Institutes of Health, Bethesda, MD, USA.
    Nyholm, Dag
    Neurology, Neuroscience, Uppsala University, Uppsala, Sweden.
    Automatic spiral analysis for objective assessment of motor symptoms in Parkinson's disease2015Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    Objective: To develop a method for objective quantification of PD motor symptoms related to Off episodes and peak dose dyskinesias, using spiral data gathered by using a touch screen telemetry device. The aim was to objectively characterize predominant motor phenotypes (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists.

    Background: A retrospective analysis was conducted on recordings from 65 patients with advanced idiopathic PD from nine different clinics in Sweden, recruited from January 2006 until August 2010. In addition to the patient group, 10 healthy elderly subjects were recruited. Upper limb movement data were collected using a touch screen telemetry device from home environments of the subjects. Measurements with the device were performed four times per day during week-long test periods. On each test occasion, the subjects were asked to trace pre-drawn Archimedean spirals, using the dominant hand. The pre-drawn spiral was shown on the screen of the device. The spiral test was repeated three times per test occasion and they were instructed to complete it within 10 seconds. The device had a sampling rate of 10Hz and measured both position and time-stamps (in milliseconds) of the pen tip.

    Methods: Four independent raters (FB, DH, AJ and DN) used a web interface that animated the spiral drawings and allowed them to observe different kinematic features during the drawing process and to rate task performance. Initially, a number of kinematic features were assessed including ‘impairment’, ‘speed’, ‘irregularity’ and ‘hesitation’ followed by marking the predominant motor phenotype on a 3-category scale: tremor, bradykinesia and/or choreatic dyskinesia. There were only 2 test occasions for which all the four raters either classified them as tremor or could not identify the motor phenotype. Therefore, the two main motor phenotype categories were bradykinesia and dyskinesia. ‘Impairment’ was rated on a scale from 0 (no impairment) to 10 (extremely severe) whereas ‘speed’, ‘irregularity’ and ‘hesitation’ were rated on a scale from 0 (normal) to 4 (extremely severe). The proposed data-driven method consisted of the following steps. Initially, 28 spatiotemporal features were extracted from the time series signals before being presented to a Multilayer Perceptron (MLP) classifier. The features were based on different kinematic quantities of spirals including radius, angle, speed and velocity with the aim of measuring the severity of involuntary symptoms and discriminate between PD-specific (bradykinesia) and/or treatment-induced symptoms (dyskinesia). A Principal Component Analysis was applied on the features to reduce their dimensions where 4 relevant principal components (PCs) were retained and used as inputs to the MLP classifier. Finally, the MLP classifier mapped these components to the corresponding visually assessed motor phenotype scores for automating the process of scoring the bradykinesia and dyskinesia in PD patients whilst they draw spirals using the touch screen device. For motor phenotype (bradykinesia vs. dyskinesia) classification, the stratified 10-fold cross validation technique was employed.

    Results: There were good agreements between the four raters when rating the individual kinematic features with intra-class correlation coefficient (ICC) of 0.88 for ‘impairment’, 0.74 for ‘speed’, 0.70 for ‘irregularity’, and moderate agreements when rating ‘hesitation’ with an ICC of 0.49. When assessing the two main motor phenotype categories (bradykinesia or dyskinesia) in animated spirals the agreements between the four raters ranged from fair to moderate. There were good correlations between mean ratings of the four raters on individual kinematic features and computed scores. The MLP classifier classified the motor phenotype that is bradykinesia or dyskinesia with an accuracy of 85% in relation to visual classifications of the four movement disorder specialists. The test-retest reliability of the four PCs across the three spiral test trials was good with Cronbach’s Alpha coefficients of 0.80, 0.82, 0.54 and 0.49, respectively. These results indicate that the computed scores are stable and consistent over time. Significant differences were found between the two groups (patients and healthy elderly subjects) in all the PCs, except for the PC3.

    Conclusions: The proposed method automatically assessed the severity of unwanted symptoms and could reasonably well discriminate between PD-specific and/or treatment-induced motor symptoms, in relation to visual assessments of movement disorder specialists. The objective assessments could provide a time-effect summary score that could be useful for improving decision-making during symptom evaluation of individualized treatment when the goal is to maximize functional On time for patients while minimizing their Off episodes and troublesome dyskinesias.

  • 15.
    Meng, Xiangli
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Statistical bound of genetic solutions to quadratic assignment problems2015Rapport (Övrigt vetenskapligt)
    Abstract [en]

    Quadratic assignment problems (QAPs) are commonly solved by heuristic methods, where the optimum is sought iteratively. Heuristics are known to provide good solutions but the quality of the solutions, i.e., the confidence interval of the solution is unknown. This paper uses statistical optimum estimation techniques (SOETs) to assess the quality of Genetic algorithm solutions for QAPs. We examine the functioning of different SOETs regarding biasness, coverage rate and length of interval, and then we compare the SOET lower bound with deterministic ones. The commonly used deterministic bounds are confined to only a few algorithms. We show that, the Jackknife estimators have better performance than Weibull estimators, and when the number of heuristic solutions is as large as 100, higher order JK-estimators perform better than lower order ones. Compared with the deterministic bounds, the SOET lower bound performs significantly better than most deterministic lower bounds and is comparable with the best deterministic ones. 

  • 16. Rahman, Asif
    et al.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Simulation and optimisation techniques for sawmill yard operation: A literature review2014Ingår i: Journal of Intelligent Learning Systems and Applications, ISSN 2150-8410, Vol. 6, nr 1, s. 21-34Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Increasing costs and competitive business strategies are pushing sawmill enterprises to make an effort for optimization of their process management. Organizational decisions mainly concentrate on performance and reduction of operational costs in order to maintain profit margins. Although many efforts have been made, effective utilization of resources, optimal planning and maximum productivity in sawmill are still challenging to sawmill industries. Many researchers proposed the simulation models in combination with optimization techniques to address problems of integrated logistics optimization. The combination of simulation and optimization technique identifies the optimal strategy by simulating all complex behaviours of the system under consideration including objectives and constraints. During the past decade, an enormous number of studies were conducted to simulate operational inefficiencies in order to find optimal solutions. This paper gives a review on recent developments and challenges associated with simulation and optimization techniques. It was believed that the review would provide a perfect ground to the authors in pursuing further work in optimizing sawmill yard operations.

  • 17.
    Ritola, Richard
    Högskolan Dalarna.
    Forensisk undersökning av Solid State Drive2012Självständigt arbete på grundnivå (kandidatexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [sv]

    Solid State Diskar (SSD) är relativt nya och mycket om dem är okänt. Detta examensarbete fokuserar på att läsa forensiskt viktig information från både lagringsutrymmet och reservutrymmet. Detta har försökts genom att ett program har byggts i C++, detta program använder ATA kommandon för att läsa information från disken. Även om programmet aldrig blev färdigt kunde det skicka och ta emot data från en SSD, dock inte reservutrymmet vilket var fokus i detta examensarbete.

  • 18.
    Sadikov, Aleksander
    et al.
    Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, Ljubljana, Slovenia .
    Žabkar, Jure
    Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, Ljubljana, Slovenia .
    Možina, Martin
    Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, Ljubljana, Slovenia .
    Groznik, Vida
    Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, Ljubljana, Slovenia .
    Nyholm, Dag
    Dept. of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Memedi, Mevludin
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Feasibility of spirography features for objective assessment of motor symptoms in Parkinson's disease2015Ingår i: Artificial Intelligence in Medicine: 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015. Proceedings / [ed] John Holmes, Riccardo Bellazzi, Lucia Sacchi and Niels Peek, Springer, 2015, Vol. 9105, s. 267-276Konferensbidrag (Refereegranskat)
    Abstract [en]

    Parkinsons disease (PD) is currently incurable, however the proper treatment can ease the symptoms and significantly improve the quality of patients life. Since PD is a chronic disease, its efficient monitoring and management is very important. The objective of this paper is to investigate the feasibility of using the features and methodology of a spirography device, originally designed to measure early Parkinsons disease (PD) symptoms, for assessing motor symptoms of advanced PD patients suffering from motor fluctuations. More specifically, the aim is 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). The work combines 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 target outcome was to learn to predict the “cause” of upper limb motor dysfunctions as assessed by a clinician who observed animated spirals in a web interface. Using the machine learning methods with feature descriptions from the Slovenian application resulted in 86% classification accuracy and over 90% AUC, demonstrating the usefulness of this approach for objective monitoring of PD patients.

  • 19.
    Shah, Syed Fawad Ali
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Intelligent Algorithms for a Hybrid FuelCell/Photovoltaic Standalone System: Simulation Of Hybrid FuelCell/Photovoltaic Standalone System2010Självständigt arbete på avancerad nivå (magisterexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [en]

    The Intelligent Algorithm is designed for theusing a Battery source. The main function is to automate the Hybrid System through anintelligent Algorithm so that it takes the decision according to the environmental conditionsfor utilizing the Photovoltaic/Solar Energy and in the absence of this, Fuel Cell energy isused. To enhance the performance of the Fuel Cell and Photovoltaic Cell we used batterybank which acts like a buffer and supply the current continuous to the load.

    To develop the main System whlogic based controller was used. Fuzzy Logic based controller used to develop this system,because they are chosen to be feasible for both controlling the decision process and predictingthe availability of the available energy on the basis of current Photovoltaic and Battery conditions.

    The Intelligent Algorithm is designed to optimize the performance of the system and to selectthe best available energy source(s) in regard of the input parameters. The enhance function of these Intelligent Controller is to predict the use of available energy resources and turn on thatparticular source for efficient energy utilization. A fuzzy controller was chosen to take thedecisions for the efficient energy utilization from the given resources. The fuzzy logic basedcontroller is designed in the Matlab-Simulink environment. Initially, the fuzzy based ruleswere built. Then MATLAB based simulation system was designed and implemented. Thenthis whole proposed model is simulated and tested for the accuracy of design and performanceof the system.

  • 20.
    Stefan, Vlad
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Discrete Event Simulation of a Sawmill Yard Using Multi-Agent System2011Självständigt arbete på avancerad nivå (magisterexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [en]

    In direct reference to the saying “time is money”, nowadays scenario simulations play a key role in the tasks people perform. Optimizing the time length of tasks and synchronizing them properly is essential to increased profits in any line of business.

    In this thesis the Bergkvist-Insjön sawmill yard process will be computer simulated. As the number of trucks arriving at the sawmill is unknown, the unexpected arrival of trucks would produce a high pressure on internal resources and handling operations. The aim of this paper is to optimize the usage of the resources in the Bergkvist-Insjön sawmill, by running three different scenarios built based on the real system simulation.

    Scenario number three, in which a log stacker only has the tasks to unload the trucks and supply the measurement station, has been found most efficient. In the simulation of this scenario, the number of logs processed by the sawmill is the highest one. Also, the time spent by the log stackers between their tasks is the shortest one from all scenarios.

    The results of this thesis revealed that the most efficient improvement of the sawmill yard would be gained by a different tasks’ priority for the operating log stackers.

  • 21.
    Westberg, Sofia
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Forensiska Undersökningar av Molntjänster2012Självständigt arbete på grundnivå (kandidatexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [sv]

    Användning av molntjänster har gjort forensiska undersökningar mer komplicerade. Däremot finns det goda förutsättningar om molnleverantörerna skapar tjänster för att få ut all information. Det skulle göra det enklare och mer tillförlitligt.

    Informationen som ska tas ut från molntjänsterna är svår att få ut på ett korrekt sätt. Undersökningen görs inte på en skrivskyddad kopia, utan i en miljö som riskerar att förändras. Det är då möjligt att ändringar görs under tiden datan hämtas ut, vilket inte alltid syns. Det går heller inte att jämföra skillnaderna genom att ta hashsummor på filerna som görs vid forensiska undersökningar av datorer. Därför är det viktigt att dokumentera hur informationen har tagits ut, helst genom att filma datorskärmen under tiden informationen tas ut.

    Informationen finns sparad på flera platser då molntjänsterna Office 365 och Google Apps används, både i molnet och på den eller de datorer som har använts för att ansluta till molntjänsten. Webbläsare sparar mycket information om vad som har gjorts. Därför är det viktigt att det går att ta reda på vilka datorer som har använts för att ansluta sig till molntjänsten, vilket idag inte möjligt. Om det är möjligt att undersöka de datorer som använts kan bevis som inte finns kvar i molnet hittas.

    Det bästa ur forensisk synvinkel skulle vara om leverantörerna av molntjänster erbjöd en tjänst som hämtar ut all data som rör en användare, inklusive alla relevanta loggar. Då skulle det ske på ett mycket säkrare sätt, då det inte skulle gå att ändra informationen under tiden den hämtas ut. 

  • 22.
    Westin, Jerker
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Nyholm, Dag
    Pålhagen, Sven
    Willows, Thomas
    Groth, Torgny
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Karlsson, Mats
    A pharmacokinetic-pharmacodynamic model for duodenal levodopa infusion2011Ingår i: Clinical neuropharmacology, ISSN 0362-5664, E-ISSN 1537-162X, Vol. 32, nr 2, s. 61-65Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Objective: The purpose of this work was to identify and estimate a population pharmacokinetic- pharmacodynamic model for duodenal infusion of a levodopa/carbidopa gel (Duodopa) to examine pharmacological properties of this treatment.

    Methods: The modeling involved pooling data from 3 studies (on advanced Parkinson disease) and fixing some parameters to values found in literature. The first study involved 12 patients studied on 3 occasions each and was previously published. The second study involved 3 patients on 2 occasions. A bolus dose was given after a washout during night. Plasma samples and motor ratings (clinical assessment of motor function on a 7-point treatment response scale ranging from "very off" to "very hyperkinetic") were collected until the clinical effect returned to baseline. The third study involved 5 patients on 3 occasions receiving 5 different dose levels. Different structural models were evaluated using the nonlinear mixed-effects modeling program NONMEM VI. Population mean parameter values, and interindividual, interoccasion, and residual variabilities were estimated.

    Results: Absorption of the levodopa/carbidopa gel can be adequately described with first-order absorption with bioavailability and lag time. Estimated population parameter values were a mean absorption time of 28.5 minutes, a lag time of 2.9 minutes, and a bioavailability of 88%. The pharmacodynamic model for motor ratings had the following population values: a half-life of effect delay of 21 minutes, a concentration at 50% effect of 1.55 mg/L, an Emax of 2.39 U on the treatment response scale, and a sigmoidicity of the Emax function of 11.6.

    Conclusions: For the typical unmedicated subject, it will take 51.4 minutes until the peak levodopa effect is reached after a bolus dose. This delay is, like the magnitude of the effect, highly variable in this patient group. The residual error magnitudes of 20% for levodopa concentrations and 0.92 U (SD) for motor ratings indicate that the models developed provide predictions of a relevant quality. The developed model may be a first step toward model-guided treatment individualization of duodenal infusion of levodopa.

  • 23.
    Yella, Siril
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Automatically detecting the number of logs on a timber truck2013Ingår i: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 22, nr 4, s. 417-435Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This article describes a method of automatically detecting, counting and classifying logs on a timber truck using a photograph (taken by the driver). An image-processing algorithm is developed to process the photograph to calculate an estimate of the number of logs present and their respective diameters. The algorithm uses color information in multiple color spaces as well as geometrical operators to segment the image and extract the relevant information. This information enables the sawmill to better plan internal logistics and production in advance of the truck’s arrival time. The algorithm is robust with respect to external factors such as varying lighting conditions and camera angle, but some inaccuracies remain, mainly caused by logs being occluded or covered in mud or snow.

  • 24.
    Yella, Siril
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Gupta, Narendra
    Fuzzy logic approach for automating visual condition monitoring of railway sleepers2007Ingår i: IICAI, Indian International Conference on Artificial Intelligence, Pune, India, 2007Konferensbidrag (Övrigt vetenskapligt)
  • 25.
    Yella, Siril
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Gupta, Naredra K.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Automating condition monitoring of wooden railway sleepers2007Ingår i: EngineerIT, ISSN 1991-5047, Vol. 2, nr 10Artikel i tidskrift (Övrigt vetenskapligt)
  • 26.
    Yella, Siril
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Shaik, Asif ur Rahman
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Pattern recognition for classifying the condition of wooden railway sleepers2010Ingår i: Multimedia Computing and Information Technology (MCIT), 2010 International Conference on Multimedia Computing and Information Technology, Sharjah, 2010, s. 61-64Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper summarises the results of using a pattern recognition approach for classifying the condition of wooden railway sleepers. Railway sleeper inspections are currently done manually; visual inspection being the most common approach, with some deeper examination using an axe to judge the condition. Digital images of the sleepers were acquired to compensate for the human visual capabilities. Appropriate image analysis techniques were applied to further process the images and necessary features such as number of cracks, crack length etc have been extracted. Finally a pattern recognition and classification approach has been adopted to further classify the condition of the sleeper into classes (good or bad). A Support Vector Machine (SVM) using a Gaussian kernel has achieved good classification rate (86%) in the current case.

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