Recurrent Neural Networks For Stroke Rehabilitation in Virtual Reality

Current virtual stroke rehabilitation system lacks rehabilitating both the impaired fingers and upper limb at one time. Most of the virtual rehabilitation modules require the attachment of wires and sensors to the body. Besides, they also lack patient performance outcome prediction using temporal in...

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Main Author:	Lim, Choon Chen
Format:	Thesis
Published:	2021
Subjects:	QA71-90 Instruments and machines
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id	my-mmu-ep.11430
record_format	uketd_dc
spelling	my-mmu-ep.114302023-05-22T08:17:50Z Recurrent Neural Networks For Stroke Rehabilitation in Virtual Reality 2021-02 Lim, Choon Chen QA71-90 Instruments and machines Current virtual stroke rehabilitation system lacks rehabilitating both the impaired fingers and upper limb at one time. Most of the virtual rehabilitation modules require the attachment of wires and sensors to the body. Besides, they also lack patient performance outcome prediction using temporal information. Hence, an attractive and immersive rehabilitation system that applies contactless sensors in detecting motion is developed. The system also incorporates a patient performance prediction system that applies artificial intelligence technologies to estimate the rehabilitation outcomes. The first game application, ‘Pick & Place’ is integrated with a Leap Motion sensor to offer finger motor rehabilitation training. Besides, the second game application of ‘Stone Breaker’ utilises Microsoft Kinect sensor to provide upper limb motor training. The visual-based rehabilitation system is equipped with a machine learning method, namely, recurrent neural network (RNN). It is employed to closely track the performance of stroke patients when conducting the rehabilitation games. The recurrent neural network is trained by using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm and Levenberg-Marquardt (LM) algorithm. A total of 60 participants (30 stroke patients and 30 control participants) are invited to join the experimental study. The stroke patients have achieved tremendous improvement when the difference of result (in Pick & Place and Stone Breaker) with the control group becomes minimal at the end of the session. Besides, the stroke group shows an excellent improvement in muscle power. The average grip strength is upgraded by 68%, and the upper limb power is improved by 11%. Moreover, the RNN exhibits a high accuracy performance in predicting the performance of the patients. The BFGS algorithm has a better predictive capability than the LM algorithm. It achieves a high average accuracy of 90.17% in Pick & Place and 93.60% in Stone Breaker in the performance estimation. The rehabilitation results also illustrate the existence of a prediction system in the rehabilitation module helps to enhance the patients’ performance. The RNN in this project outperforms four existing techniques that apply RNN to predict medical events. All the results show the potential to integrate the visual-based rehabilitation with the patient performance prediction system in restoring stroke patients’ impaired limbs. 2021-02 Thesis http://shdl.mmu.edu.my/11430/ http://erep.mmu.edu.my/ masters Multimedia University Faculty of Engineering and Technology (FET) EREP ID: 9907
institution	Multimedia University
collection	MMU Institutional Repository
topic	QA71-90 Instruments and machines
spellingShingle	QA71-90 Instruments and machines Lim, Choon Chen Recurrent Neural Networks For Stroke Rehabilitation in Virtual Reality
description	Current virtual stroke rehabilitation system lacks rehabilitating both the impaired fingers and upper limb at one time. Most of the virtual rehabilitation modules require the attachment of wires and sensors to the body. Besides, they also lack patient performance outcome prediction using temporal information. Hence, an attractive and immersive rehabilitation system that applies contactless sensors in detecting motion is developed. The system also incorporates a patient performance prediction system that applies artificial intelligence technologies to estimate the rehabilitation outcomes. The first game application, ‘Pick & Place’ is integrated with a Leap Motion sensor to offer finger motor rehabilitation training. Besides, the second game application of ‘Stone Breaker’ utilises Microsoft Kinect sensor to provide upper limb motor training. The visual-based rehabilitation system is equipped with a machine learning method, namely, recurrent neural network (RNN). It is employed to closely track the performance of stroke patients when conducting the rehabilitation games. The recurrent neural network is trained by using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm and Levenberg-Marquardt (LM) algorithm. A total of 60 participants (30 stroke patients and 30 control participants) are invited to join the experimental study. The stroke patients have achieved tremendous improvement when the difference of result (in Pick & Place and Stone Breaker) with the control group becomes minimal at the end of the session. Besides, the stroke group shows an excellent improvement in muscle power. The average grip strength is upgraded by 68%, and the upper limb power is improved by 11%. Moreover, the RNN exhibits a high accuracy performance in predicting the performance of the patients. The BFGS algorithm has a better predictive capability than the LM algorithm. It achieves a high average accuracy of 90.17% in Pick & Place and 93.60% in Stone Breaker in the performance estimation. The rehabilitation results also illustrate the existence of a prediction system in the rehabilitation module helps to enhance the patients’ performance. The RNN in this project outperforms four existing techniques that apply RNN to predict medical events. All the results show the potential to integrate the visual-based rehabilitation with the patient performance prediction system in restoring stroke patients’ impaired limbs.
format	Thesis
qualification_level	Master's degree
author	Lim, Choon Chen
author_facet	Lim, Choon Chen
author_sort	Lim, Choon Chen
title	Recurrent Neural Networks For Stroke Rehabilitation in Virtual Reality
title_short	Recurrent Neural Networks For Stroke Rehabilitation in Virtual Reality
title_full	Recurrent Neural Networks For Stroke Rehabilitation in Virtual Reality
title_fullStr	Recurrent Neural Networks For Stroke Rehabilitation in Virtual Reality
title_full_unstemmed	Recurrent Neural Networks For Stroke Rehabilitation in Virtual Reality
title_sort	recurrent neural networks for stroke rehabilitation in virtual reality
granting_institution	Multimedia University
granting_department	Faculty of Engineering and Technology (FET)
publishDate	2021
_version_	1776101406482104320

Recurrent Neural Networks For Stroke Rehabilitation in Virtual Reality

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