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  • 1.
    Aghanavesi, Somayeh
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Bergquist, Filip
    Gothenburg University.
    Nyholm, Dag
    Uppsala University.
    Senek, Marina
    Uppsala University.
    Memedi, Mevludin
    Örebro University.
    Objective assessment of Parkinson’s disease motor symptoms during leg agility test using motion sensors2018Conference paper (Refereed)
    Abstract [en]

    Title: Objective assessment of Parkinson’s disease motor symptoms during leg agility test using motion sensors

    Objective: To develop and evaluate machine learning methods for assessment of Parkinson’s disease (PD) motor symptoms using leg agility (LA) data collected with motion sensors during a single dose experiment.

    Background: Nineteen advanced PD patients (Gender: 14 males and 5 females, mean age: 71.4, mean years with PD: 9.7, mean years with levodopa: 9.5) were recruited in a single center, open label, single dose clinical trial in Sweden [1].

    Methods: The patients performed up to 15 LA tasks while wearing motions sensors on their foot ankle. They performed tests at pre-defined time points starting from baseline, at the time they received a morning dose (150% of their levodopa equivalent morning dose), and at follow-up time points until the medication wore off. The patients were video recorded while performing the motor tasks. and three movement disorder experts rated the observed motor symptoms using 4 items from the Unified PD Rating Scale (UPDRS) motor section including UPDRS #26 (leg agility), UPDRS #27 (Arising from chair), UPDRS #29 (Gait), UPDRS #31 (Body Bradykinesia and Hypokinesia), and dyskinesia scale. In addition, they rated the overall mobility of the patients using Treatment Response Scale (TRS), ranging from -3 (very off) to 3 (very dyskinetic). Sensors data were processed and their quantitative measures were used to develop machine learning methods, which mapped them to the mean ratings of the three raters. The quality of measurements of the machine learning methods was assessed by convergence validity, test-retest reliability and sensitivity to treatment.

    Results: Results from the 10-fold cross validation showed good convergent validity of the machine learning methods (Support Vector Machines, SVM) with correlation coefficients of 0.81 for TRS, 0.78 for UPDRS #26, 0.69 for UPDRS #27, 0.78 for UPDRS #29, 0.83 for UPDRS #31, and 0.67 for dyskinesia scale (P<0.001). There were good correlations between scores produced by the methods during the first (baseline) and second tests with coefficients ranging from 0.58 to 0.96, indicating good test-retest reliability. The machine learning methods had lower sensitivity than mean clinical ratings (Figure. 1).

    Conclusions: The presented methodology was able to assess motor symptoms in PD well, comparable to movement disorder experts. The leg agility test did not reflect treatment related changes.

  • 2.
    Aghanavesi, Somayeh
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Filip, Bergquist
    Gothenburg University.
    Nyholm, Dag
    Uppsala University.
    Senek, Marina
    Uppsala University.
    Memedi, Mevludin
    Örebro University.
    Feasibility of a multi-sensor data fusion method for assessment of Parkinson’s disease motor symptoms2018Conference paper (Refereed)
    Abstract [en]

    Title: Feasibility of a multi-sensor data fusion method for assessment of Parkinson’s disease motor symptoms

    Objective: To assess the feasibility of measuring Parkinson’s disease (PD) motor symptoms with a multi-sensor data fusion method. More specifically, the aim is to assess validity, reliability and sensitivity to treatment of the methods.

    Background: Data from 19 advanced PD patients (Gender: 14 males and 5 females, mean age: 71.4, mean years with PD: 9.7, mean years with levodopa: 9.5) were collected in a single center, open label, single dose clinical trial in Sweden [1].

    Methods: The patients performed leg agility and 2-5 meter straight walking tests while wearing motion sensors on their limbs. They performed the tests at baseline, at the time they received the morning dose, and at pre-specified time points until the medication wore off. While performing the tests the patients were video recorded. The videos were observed by three movement disorder specialists who rated the symptoms using a treatment response scale (TRS), ranging from -3 (very off) to 3 (very dyskinetic). The sensor data consisted of lower limb data during leg agility, upper limb data during walking, and lower limb data during walking. Time series analysis was performed on the raw sensor data extracted from 17 patients to derive a set of quantitative measures, which were then used during machine learning to be mapped to mean ratings of the three raters on the TRS scale. Combinations of data were tested during the machine learning procedure.

    Results: Using data from both tests, the Support Vector Machines (SVM) could predict the motor states of the patients on the TRS scale with a good agreement in relation to the mean ratings of the three raters (correlation coefficient = 0.92, root mean square error = 0.42, p<0.001). Additionally, there was good test-retest reliability of the SVM scores during baseline and second tests with intraclass-correlation coefficient of 0.84. Sensitivity to treatment for SVM was good (Figure 1), indicating its ability to detect changes in motor symptoms. The upper limb data during walking was more informative than lower limb data during walking since SVMs had higher correlation coefficient to mean ratings.  

    Conclusions: The methodology demonstrates good validity, reliability, and sensitivity to treatment. This indicates that it could be useful for individualized optimization of treatments among PD patients, leading to an improvement in health-related quality of life.

  • 3.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Traffic sign recognition without color information2015In: Colour and Visual Computing Symposium (CVCS), 2015 / [ed] Pedersen, M; Thomas, JB, IEEE conference proceedings, 2015, p. 1-6Conference paper (Refereed)
    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 which represent a challenge to the proposed approach.

  • 4.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Davami, Erfan
    Jomaa, Diala
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Segmentation of fingerprint images based on bi-level processing using fuzzy rules2012In: Fuzzy Information Processing Society (NAFIPS), 2012 Annual Meeting of the North American, 2012, p. 1-6Conference paper (Refereed)
    Abstract [en]

    This paper presents a new approach to segment low quality fingerprint images which are collected by low quality fingerprint readers. Images collected using such readers are easy to collect but difficult to segment. The proposed approach is based on combining global and local processing to achieve segmentation of fingerprint images. On the global level, the fingerprint is located and extracted from the rest of the image by using a global thresholding 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 different fuzzy rules which the two images are segmented. These rules are based on the mean and variance of the block under consideration. The approach is implemented in three stages; preprocessing, segmentation, and post-processing. Segmentation of 100 images was performed and compared with manual examinations by human experts. The experiments showed that 96% of images under test are correctly segmented. The results from the quality of segmentation test revealed that the average error in block segmentation was 2.84% and the false positive and false negatives were approximately 1.4%. This indicates the high robustness of the proposed approach.

  • 5.
    Hansson, Karl
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Yella, Siril
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Machine Learning Algorithms in Heavy Process Manufacturing2016In: American Journal of Intelligent Systems, ISSN 2165-8978, E-ISSN 2165-8994, Vol. 6, no 1, p. 1-13Article in journal (Refereed)
    Abstract [en]

    In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.

  • 6.
    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.

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