| 1. |
- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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Feasibility of Using Dynamic Time Warping to Measure Motor States in Parkinson’s Disease
- 2020
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Ingår i: Journal of Sensors. - London : Hindawi Publishing Corporation. - 1687-725X .- 1687-7268. ; , s. 1-14
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this paper is to investigate the feasibility of using the Dynamic Time Warping (DTW) method to measure motor states in advanced Parkinson's disease (PD). Data were collected from 19 PD patients who experimented leg agility motor tests with motion sensors on their ankles once before and multiple times after an administration of 150% of their normal daily dose of medication. Experiments of 22 healthy controls were included. Three movement disorder specialists rated the motor states of the patients according to Treatment Response Scale (TRS) using recorded videos of the experiments. A DTW-based motor state distance score (DDS) was constructed using the acceleration and gyroscope signals collected during leg agility motor tests. Mean DDS showed similar trends to mean TRS scores across the test occasions. Mean DDS was able to differentiate between PD patients at Off and On motor states. DDS was able to classify the motor state changes with good accuracy (82%). The PD patients who showed more response to medication were selected using the TRS scale, and the most related DTW-based features to their TRS scores were investigated. There were individual DTW-based features identified for each patient. In conclusion, the DTW method can provide information about motor states of advanced PD patients which can be used in the development of methods for automatic motor scoring of PD. © 2020 Somayeh Aghanavesi et al.
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| 2. |
- Al-Hammadi, Mustafa, 1995-, et al.
(författare)
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Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis : A Systematic Literature Review
- 2024
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Ingår i: Journal of Alzheimer's Disease. - : IOS Press. - 1387-2877 .- 1875-8908. ; 100:1, s. 1-27
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Forskningsöversikt (refereegranskat)abstract
- Background:Dementia is a general term for several progressive neurodegenerative disorders including Alzheimer’s disease. Timely and accurate detection is crucial for early intervention. Advancements in artificial intelligence present significant potential for using machine learning to aid in early detection.Objective:Summarize the state-of-the-art machine learning-based approaches for dementia prediction, focusing on non-invasive methods, as the burden on the patients is lower. Specifically, the analysis of gait and speech performance can offer insights into cognitive health through clinically cost-effective screening methods.Methods:A systematic literature review was conducted following the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The search was performed on three electronic databases (Scopus, Web of Science, and PubMed) to identify the relevant studies published between 2017 to 2022. A total of 40 papers were selected for review.Results:The most common machine learning methods employed were support vector machine followed by deep learning. Studies suggested the use of multimodal approaches as they can provide comprehensive and better prediction performance. Deep learning application in gait studies is still in the early stages as few studies have applied it. Moreover, including features of whole body movement contribute to better classification accuracy. Regarding speech studies, the combination of different parameters (acoustic, linguistic, cognitive testing) produced better results.Conclusions:The review highlights the potential of machine learning, particularly non-invasive approaches, in the early prediction of dementia. The comparable prediction accuracies of manual and automatic speech analysis indicate an imminent fully automated approach for dementia detection.
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| 3. |
- Fleyeh, Hasan
(författare)
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Traffic sign recognition without color information
- 2015
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Ingår i: Colour and Visual Computing Symposium (CVCS), 2015. - Borlänge : Högskolan Dalarna. ; , s. 1-6
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 4. |
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| 5. |
- Aghanavesi, Somayeh, 1981-
(författare)
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Smartphone-based Parkinson’s disease symptom assessment
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 6. |
- Biswas, Rubel, et al.
(författare)
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Detection and classification of speed limit traffic signs
- 2014
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Ingår i: 2014 World Congree on Computer Applications and Information Systems (WCCAIS). - 9781479933518
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Konferensbidrag (refereegranskat)abstract
- This paper presents a novel traffic sign recognition system which can aid in the development of Intelligent Speed Adaptation. This system is based on extracting the speed limit sign from the traffic scene by Circular Hough Transform (CHT) with the aid of colour and non-colour information of the traffic sign. The digits of the speed limit sign are then extracted and classified using SVM classifier which is trained for this purpose. In general, the system detects the prohibitory traffic sign in the first place, specifies whether the detected sign is a speed limit sign, and then determines the allowed speed in case the detected sign is a speed limit sign. The SVM classifier was trained with 270 images which were collected in different light conditions. To check the robustness of this system, it was tested against 210 images which contain 213 speed limit traffic sign and 288 Non-Speed limit signs. It was found that the accuracy of recognition was 98% which indicates clearly the high robustness targeted by this system.
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| 7. |
- Davami, Erfan, et al.
(författare)
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Classification with NormalBoost
- 2011
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Ingår i: Journal of Intelligent Systems. - London : de Gruyter Reference Global. - 0334-1860 .- 2191-026X. ; 20:2, s. 187-208
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 8. |
- Fleyeh, Hasan, et al.
(författare)
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A contour-based separation of vertically attached traffic signs
- 2008
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Ingår i: 34th Annual Conference of the IEEE Industrial Electronics Society, vols 1-5, proceedings. ; , s. 1747-1752
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Konferensbidrag (refereegranskat)abstract
- This paper presents a contour-based approach to separate vertically attached traffic signs. The algorithm is based on using binary images which are generated by any color segmentation algorithm to represent objects which could be candidate traffic signs. Since all traffic signs are similar about their vertical axis, an improved cross-correlation algorithm is invoked to determine this similarity and filters traffic sign candidates. Shape decomposition is used to smooth the contour of the candidate object iteratively in order to reduce white noise. Flipping point detection algorithm which locates black noise along the smoothed contour and the curve prediction algorithm are invoked to determine the final cut points. A separation accuracy of 94% is achieved by the algorithm. In this experiment more that 70000 images of different traffic sign combinations are invoked to achieve this result. The algorithm is tested on one-sign images, two-sign images, and three-sign images which are combined together for the purpose of testing this algorithm.
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| 9. |
- Fleyeh, Hasan
(författare)
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A novel fuzzy approach for shape determination of traffic signs
- 2005
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Ingår i: Second Indian International Conference on Arificial Intelligence. - Pune, India.
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Konferensbidrag (refereegranskat)abstract
- In this paper, a novel fuzzy approach is developed to determine the shape of traffic signs. More than 1600 images of traffic signs were collected in different light conditions by a digital camera mounted in a car and used for testing this approach. Every RGB image was converted into HSV colour space, and segmented by using a set of fuzzy rules depending on the hue and saturation values of each pixel in the HSV colour space. The fuzzy rules are used to extract the colours of the road signs. Objects in each segmented image are labelled and tested for the presence of probable sign. All small objects under certain threshold are discarded, and the remaining objects are tested by a fuzzy shape recognizer which invokes another set of fuzzy rules. Four shape measures are used to decide the shape of the sign; rectangularity, triangularity, ellipticity, and the new shape measure octagonality.
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| 10. |
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