Leveraging mechanomyography signal for quantitative muscle spasticity assessment of upper limb in neurological disorders using machine learning
Upper motor neuron syndrome is characterised by spasticity, which represents a neurological disability that can be found in several disorders such as cerebral palsy, amyotrophic lateral sclerosis, stroke, brain injury, and spinal cord injury. Muscle spasticity is always assessed by therapists using...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Science and Information Organization
2024
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| Online Access: | https://umpir.ump.edu.my/id/eprint/44219/ |
| _version_ | 1848827315920830464 |
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| author | Muhamad Aliff Imran, Daud Asmarani Ahmad Puzi, asmarani@iium.edu.my Shahrul Na’im, Sidek Ahmad Anwar, Zainuddin Ismail, Mohd Khairuddin Mohd Azri, Abd Mutalib |
| author_facet | Muhamad Aliff Imran, Daud Asmarani Ahmad Puzi, asmarani@iium.edu.my Shahrul Na’im, Sidek Ahmad Anwar, Zainuddin Ismail, Mohd Khairuddin Mohd Azri, Abd Mutalib |
| author_sort | Muhamad Aliff Imran, Daud |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Upper motor neuron syndrome is characterised by spasticity, which represents a neurological disability that can be found in several disorders such as cerebral palsy, amyotrophic lateral sclerosis, stroke, brain injury, and spinal cord injury. Muscle spasticity is always assessed by therapists using conventional methods involving passive movement and assigning spasticity grades to the relevant joints based on the degree of muscle resistance which leads to inconsistency in assessment and could affect the efficiency of the rehabilitation process. To address this problem, the study proposed to develop a muscle spasticity model using Mechanomyography (MMG) signals from the forearm muscles. The muscle spasticity model leveraged based on the Modified Ashworth Scale and focus on flexion and extension movements of the forearm. Thirty subjects who satisfied the requirements and provided consent were recruited to participate in the data collection. The data underwent a pre-processing stage and was subsequently analysed prior to the extraction of features. The dataset consists of forty-eight extracted features from the three-direction x, y, z axes (for both biceps and triceps muscle), corresponding to the longitudinal, lateral, and transverse orientations relative to the muscle fibers. Significant features selection was conducted to analyse if overall significant difference showed in the combined set of these features across the different spasticity levels. The test results determined the selection of twenty-five features from a total of forty-eight which be used to train an optimum classifier algorithm for the purpose of quantifying the level of muscle spasticity. Linear Discriminant Analysis (LDA), Decision Trees (DTs), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN) algorithms have been employed to achieve better accuracy in quantifying the muscle spasticity level. The KNN-based classifier achieved the highest performance, with an accuracy of 91.29% with k=15, surpassing the accuracy of other classifiers. This leads to consistency in spasticity evaluation, hence offering optimum rehabilitation strategies. |
| first_indexed | 2025-11-15T03:58:46Z |
| format | Article |
| id | ump-44219 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:58:46Z |
| publishDate | 2024 |
| publisher | Science and Information Organization |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-442192025-09-29T07:36:44Z https://umpir.ump.edu.my/id/eprint/44219/ Leveraging mechanomyography signal for quantitative muscle spasticity assessment of upper limb in neurological disorders using machine learning Muhamad Aliff Imran, Daud Asmarani Ahmad Puzi, asmarani@iium.edu.my Shahrul Na’im, Sidek Ahmad Anwar, Zainuddin Ismail, Mohd Khairuddin Mohd Azri, Abd Mutalib RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry TS Manufactures Upper motor neuron syndrome is characterised by spasticity, which represents a neurological disability that can be found in several disorders such as cerebral palsy, amyotrophic lateral sclerosis, stroke, brain injury, and spinal cord injury. Muscle spasticity is always assessed by therapists using conventional methods involving passive movement and assigning spasticity grades to the relevant joints based on the degree of muscle resistance which leads to inconsistency in assessment and could affect the efficiency of the rehabilitation process. To address this problem, the study proposed to develop a muscle spasticity model using Mechanomyography (MMG) signals from the forearm muscles. The muscle spasticity model leveraged based on the Modified Ashworth Scale and focus on flexion and extension movements of the forearm. Thirty subjects who satisfied the requirements and provided consent were recruited to participate in the data collection. The data underwent a pre-processing stage and was subsequently analysed prior to the extraction of features. The dataset consists of forty-eight extracted features from the three-direction x, y, z axes (for both biceps and triceps muscle), corresponding to the longitudinal, lateral, and transverse orientations relative to the muscle fibers. Significant features selection was conducted to analyse if overall significant difference showed in the combined set of these features across the different spasticity levels. The test results determined the selection of twenty-five features from a total of forty-eight which be used to train an optimum classifier algorithm for the purpose of quantifying the level of muscle spasticity. Linear Discriminant Analysis (LDA), Decision Trees (DTs), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN) algorithms have been employed to achieve better accuracy in quantifying the muscle spasticity level. The KNN-based classifier achieved the highest performance, with an accuracy of 91.29% with k=15, surpassing the accuracy of other classifiers. This leads to consistency in spasticity evaluation, hence offering optimum rehabilitation strategies. Science and Information Organization 2024 Article PeerReviewed pdf en cc_by_4 https://umpir.ump.edu.my/id/eprint/44219/1/Leveraging%20mechanomyography%20signal%20for%20quantitative%20muscle.pdf Muhamad Aliff Imran, Daud and Asmarani Ahmad Puzi, asmarani@iium.edu.my and Shahrul Na’im, Sidek and Ahmad Anwar, Zainuddin and Ismail, Mohd Khairuddin and Mohd Azri, Abd Mutalib (2024) Leveraging mechanomyography signal for quantitative muscle spasticity assessment of upper limb in neurological disorders using machine learning. International Journal of Advanced Computer Science and Applications (IJACSA), 15 (8). pp. 991-1002. ISSN 2158-107X. (Published) https://doi.org/10.14569/IJACSA.2024.0150898 https://doi.org/10.14569/IJACSA.2024.0150898 https://doi.org/10.14569/IJACSA.2024.0150898 |
| spellingShingle | RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry TS Manufactures Muhamad Aliff Imran, Daud Asmarani Ahmad Puzi, asmarani@iium.edu.my Shahrul Na’im, Sidek Ahmad Anwar, Zainuddin Ismail, Mohd Khairuddin Mohd Azri, Abd Mutalib Leveraging mechanomyography signal for quantitative muscle spasticity assessment of upper limb in neurological disorders using machine learning |
| title | Leveraging mechanomyography signal for quantitative muscle spasticity assessment of upper limb in neurological disorders using machine learning |
| title_full | Leveraging mechanomyography signal for quantitative muscle spasticity assessment of upper limb in neurological disorders using machine learning |
| title_fullStr | Leveraging mechanomyography signal for quantitative muscle spasticity assessment of upper limb in neurological disorders using machine learning |
| title_full_unstemmed | Leveraging mechanomyography signal for quantitative muscle spasticity assessment of upper limb in neurological disorders using machine learning |
| title_short | Leveraging mechanomyography signal for quantitative muscle spasticity assessment of upper limb in neurological disorders using machine learning |
| title_sort | leveraging mechanomyography signal for quantitative muscle spasticity assessment of upper limb in neurological disorders using machine learning |
| topic | RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry TS Manufactures |
| url | https://umpir.ump.edu.my/id/eprint/44219/ https://umpir.ump.edu.my/id/eprint/44219/ https://umpir.ump.edu.my/id/eprint/44219/ |