Spiking neural network classification for spike train analysis of physiotherapy movements
Classifying gesture or movements nowadays become a demanding business as the technologies of sensor rose. This has enchanted many researchers to actively investigated widely within the area of computer vision. Rehabilitation exercises is one of the most popular gestures or movements that being w...
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my.unimas.ir.391832022-09-29T02:01:09Z http://ir.unimas.my/id/eprint/39183/ Spiking neural network classification for spike train analysis of physiotherapy movements Fadilla 'Atyka, Nor Rashid Nor Surayahani, Suriani QA75 Electronic computers. Computer science Classifying gesture or movements nowadays become a demanding business as the technologies of sensor rose. This has enchanted many researchers to actively investigated widely within the area of computer vision. Rehabilitation exercises is one of the most popular gestures or movements that being worked by the researchers nowadays. Rehab session usually involves experts that monitored the patients but lacking the experts itself made the session become longer and unproductive. This works adopted a dataset from UI-PRMD that assembled from 10 rehabilitation movements. The data has been encoded into spike trains for spike patterns analysis. Next, we tend to train the spike trains into Spiking Neural Networks and resulting into a promising result. However, in future, this method will be tested with other data to validate the performance, also to enhance the success rate of the accuracy. 2020-02 Article PeerReviewed text en http://ir.unimas.my/id/eprint/39183/1/Spiking%20neural%20network%20classification%20for%20spike%20train%20analysis.pdf Fadilla 'Atyka, Nor Rashid and Nor Surayahani, Suriani (2020) Spiking neural network classification for spike train analysis of physiotherapy movements. Bulletin of Electrical Engineering and Informatics, 9 (1). pp. 319-325. ISSN 2302-9285 10.11591/eei.v9i1.1868 |
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QA75 Electronic computers. Computer science Fadilla 'Atyka, Nor Rashid Nor Surayahani, Suriani Spiking neural network classification for spike train analysis of physiotherapy movements |
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Classifying gesture or movements nowadays become a demanding business
as the technologies of sensor rose. This has enchanted many researchers
to actively investigated widely within the area of computer vision.
Rehabilitation exercises is one of the most popular gestures or movements
that being worked by the researchers nowadays. Rehab session usually
involves experts that monitored the patients but lacking the experts itself
made the session become longer and unproductive. This works adopted
a dataset from UI-PRMD that assembled from 10 rehabilitation movements.
The data has been encoded into spike trains for spike patterns analysis.
Next, we tend to train the spike trains into Spiking Neural Networks
and resulting into a promising result. However, in future, this method
will be tested with other data to validate the performance, also to enhance
the success rate of the accuracy. |
format |
Article |
author |
Fadilla 'Atyka, Nor Rashid Nor Surayahani, Suriani |
author_facet |
Fadilla 'Atyka, Nor Rashid Nor Surayahani, Suriani |
author_sort |
Fadilla 'Atyka, Nor Rashid |
title |
Spiking neural network classification for spike train analysis of physiotherapy movements |
title_short |
Spiking neural network classification for spike train analysis of physiotherapy movements |
title_full |
Spiking neural network classification for spike train analysis of physiotherapy movements |
title_fullStr |
Spiking neural network classification for spike train analysis of physiotherapy movements |
title_full_unstemmed |
Spiking neural network classification for spike train analysis of physiotherapy movements |
title_sort |
spiking neural network classification for spike train analysis of physiotherapy movements |
publishDate |
2020 |
url |
http://ir.unimas.my/id/eprint/39183/1/Spiking%20neural%20network%20classification%20for%20spike%20train%20analysis.pdf http://ir.unimas.my/id/eprint/39183/ |
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1745566055674150912 |
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13.149126 |