Quantitative spasticity assessment model of neurological disorder patients / AA Puzi … [et al.]

Patients with neurological disorders usually experience conditions where their muscles are stiff, tight, and prone to resist upon stretching, which in essence defines muscle spasticity. The current method of muscle spasticity assessment is based on subjective assessment by therapists who rely on the...

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Main Authors: Ahmad Puzi, Asmarani, Aliff-Imran, M.D., Zainuddin, Ahmad Anwar, Basri, Atikah Balqis, Mohd Khairuddin, Ismail
Format: Book Section
Language:English
Published: Faculty of Computer and Mathematical Sciences 2023
Subjects:
Online Access:https://ir.uitm.edu.my/id/eprint/93720/1/93720.pdf
https://ir.uitm.edu.my/id/eprint/93720/
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spelling my.uitm.ir.937202024-05-02T03:15:28Z https://ir.uitm.edu.my/id/eprint/93720/ Quantitative spasticity assessment model of neurological disorder patients / AA Puzi … [et al.] Ahmad Puzi, Asmarani Aliff-Imran, M.D. Zainuddin, Ahmad Anwar Basri, Atikah Balqis Mohd Khairuddin, Ismail Integer programming Patients with neurological disorders usually experience conditions where their muscles are stiff, tight, and prone to resist upon stretching, which in essence defines muscle spasticity. The current method of muscle spasticity assessment is based on subjective assessment by therapists who rely on their inner intuition, experience, and skills that comply with the Modified Ashworth Scale tool. This leads to inconsistency in assessment and could affect the efficacy of the rehabilitation process. Although current trends quantify the clinical assessment with some positive results, they have been shown to pose challenges in identifying the significant spasticity characteristics to produce a proficient model of muscle spasticity characteristics of neurological disorder patients by ignoring the composition of the measured signals. Thus, the research's main objective is to develop the spasticity muscle characteristics model based on Modified Ashworth Scale (MAS) scores from forearm musculature using Mechanomyography (MMG) signals. The cues from the MMG signals pattern will be used to select the sampling features for the development of the classification algorithm model. A customized non-invasive MMG device will be used to collect the signal characterizations from patients with different scores of MAS clinical assessment. It is envisaged that the main output of the research is a novel spasticity muscle characteristics MAS model-based. The impact of this research can serve significantly as the standardized and objective assessment tool for measuring the muscle spasticity level of the affected limb. Hence warranting a more effective rehabilitation process and reduction in overall expenditures pertaining to saving cost, time, and energy. Faculty of Computer and Mathematical Sciences 2023 Book Section NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/93720/1/93720.pdf Quantitative spasticity assessment model of neurological disorder patients / AA Puzi … [et al.]. (2023) In: International Jasin Multimedia & Computer Science Invention and Innovation Exhibition (i-JaMCSIIX 2023). Faculty of Computer and Mathematical Sciences, Kampus Jasin, p. 11. (Submitted)
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Integer programming
spellingShingle Integer programming
Ahmad Puzi, Asmarani
Aliff-Imran, M.D.
Zainuddin, Ahmad Anwar
Basri, Atikah Balqis
Mohd Khairuddin, Ismail
Quantitative spasticity assessment model of neurological disorder patients / AA Puzi … [et al.]
description Patients with neurological disorders usually experience conditions where their muscles are stiff, tight, and prone to resist upon stretching, which in essence defines muscle spasticity. The current method of muscle spasticity assessment is based on subjective assessment by therapists who rely on their inner intuition, experience, and skills that comply with the Modified Ashworth Scale tool. This leads to inconsistency in assessment and could affect the efficacy of the rehabilitation process. Although current trends quantify the clinical assessment with some positive results, they have been shown to pose challenges in identifying the significant spasticity characteristics to produce a proficient model of muscle spasticity characteristics of neurological disorder patients by ignoring the composition of the measured signals. Thus, the research's main objective is to develop the spasticity muscle characteristics model based on Modified Ashworth Scale (MAS) scores from forearm musculature using Mechanomyography (MMG) signals. The cues from the MMG signals pattern will be used to select the sampling features for the development of the classification algorithm model. A customized non-invasive MMG device will be used to collect the signal characterizations from patients with different scores of MAS clinical assessment. It is envisaged that the main output of the research is a novel spasticity muscle characteristics MAS model-based. The impact of this research can serve significantly as the standardized and objective assessment tool for measuring the muscle spasticity level of the affected limb. Hence warranting a more effective rehabilitation process and reduction in overall expenditures pertaining to saving cost, time, and energy.
format Book Section
author Ahmad Puzi, Asmarani
Aliff-Imran, M.D.
Zainuddin, Ahmad Anwar
Basri, Atikah Balqis
Mohd Khairuddin, Ismail
author_facet Ahmad Puzi, Asmarani
Aliff-Imran, M.D.
Zainuddin, Ahmad Anwar
Basri, Atikah Balqis
Mohd Khairuddin, Ismail
author_sort Ahmad Puzi, Asmarani
title Quantitative spasticity assessment model of neurological disorder patients / AA Puzi … [et al.]
title_short Quantitative spasticity assessment model of neurological disorder patients / AA Puzi … [et al.]
title_full Quantitative spasticity assessment model of neurological disorder patients / AA Puzi … [et al.]
title_fullStr Quantitative spasticity assessment model of neurological disorder patients / AA Puzi … [et al.]
title_full_unstemmed Quantitative spasticity assessment model of neurological disorder patients / AA Puzi … [et al.]
title_sort quantitative spasticity assessment model of neurological disorder patients / aa puzi … [et al.]
publisher Faculty of Computer and Mathematical Sciences
publishDate 2023
url https://ir.uitm.edu.my/id/eprint/93720/1/93720.pdf
https://ir.uitm.edu.my/id/eprint/93720/
_version_ 1800100585436872704
score 13.18916