Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision
The Modified Ashworth Scale (MAS) is commonly used to assess spasticity in clinics. The qualitative description of MAS has resulted in ambiguity during spasticity assessment. This work supports spasticity assessment by providing measurement data acquired from wireless wearable sensors, i.e., goniome...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Mdpi
2023
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| Online Access: | http://eprints.uthm.edu.my/9822/1/J15866_3962a9a5eedc50a7d3cf3ed014298823.pdf |
| _version_ | 1796870046599872512 |
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| author | Jingye Yee, Jingye Yee Cheng Yee Low, Cheng Yee Low Mohamad Hashim, Natiara Che Zakaria, Noor Ayuni Johar, Khairunnisa Othman, Nurul Atiqah Hock Hung Chieng, Hock Hung Chieng Hanapiah, Fazah Akhtar |
| author_facet | Jingye Yee, Jingye Yee Cheng Yee Low, Cheng Yee Low Mohamad Hashim, Natiara Che Zakaria, Noor Ayuni Johar, Khairunnisa Othman, Nurul Atiqah Hock Hung Chieng, Hock Hung Chieng Hanapiah, Fazah Akhtar |
| author_sort | Jingye Yee, Jingye Yee |
| collection | UTHM |
| description | The Modified Ashworth Scale (MAS) is commonly used to assess spasticity in clinics. The qualitative description of MAS has resulted in ambiguity during spasticity assessment. This work supports spasticity assessment by providing measurement data acquired from wireless wearable sensors, i.e., goniometers, myometers, and surface electromyography sensors. Based on in-depth discussions with consultant rehabilitation physicians, eight (8) kinematic, six (6) kinetic, and four (4) physiological features were extracted from the collected clinical data from fifty (50) subjects. These features were used to train and evaluate the conventional machine learning classifiers, including but not limited to Support Vector Machine (SVM) and Random Forest (RF). Subsequently, a spasticity classification approach combining the decision-making logic of the consultant rehabilitation physicians, SVM, and RF was developed. The empirical results on the unknown test set show that the proposed Logical–SVM–RF classifier outperforms each individual classifier, reporting an accuracy of 91% compared to 56–81% achieved by SVM and RF. A data-driven diagnosis decision contributing to interrater reliability is enabled via the availability of quantitative clinical data and a MAS prediction. |
| first_indexed | 2024-03-05T22:03:44Z |
| format | Article |
| id | uthm.eprints-9822 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T22:03:44Z |
| publishDate | 2023 |
| publisher | Mdpi |
| record_format | dspace |
| spelling | uthm.eprints-98222023-09-13T07:23:35Z http://eprints.uthm.edu.my/9822/ Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision Jingye Yee, Jingye Yee Cheng Yee Low, Cheng Yee Low Mohamad Hashim, Natiara Che Zakaria, Noor Ayuni Johar, Khairunnisa Othman, Nurul Atiqah Hock Hung Chieng, Hock Hung Chieng Hanapiah, Fazah Akhtar T Technology (General) The Modified Ashworth Scale (MAS) is commonly used to assess spasticity in clinics. The qualitative description of MAS has resulted in ambiguity during spasticity assessment. This work supports spasticity assessment by providing measurement data acquired from wireless wearable sensors, i.e., goniometers, myometers, and surface electromyography sensors. Based on in-depth discussions with consultant rehabilitation physicians, eight (8) kinematic, six (6) kinetic, and four (4) physiological features were extracted from the collected clinical data from fifty (50) subjects. These features were used to train and evaluate the conventional machine learning classifiers, including but not limited to Support Vector Machine (SVM) and Random Forest (RF). Subsequently, a spasticity classification approach combining the decision-making logic of the consultant rehabilitation physicians, SVM, and RF was developed. The empirical results on the unknown test set show that the proposed Logical–SVM–RF classifier outperforms each individual classifier, reporting an accuracy of 91% compared to 56–81% achieved by SVM and RF. A data-driven diagnosis decision contributing to interrater reliability is enabled via the availability of quantitative clinical data and a MAS prediction. Mdpi 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/9822/1/J15866_3962a9a5eedc50a7d3cf3ed014298823.pdf Jingye Yee, Jingye Yee and Cheng Yee Low, Cheng Yee Low and Mohamad Hashim, Natiara and Che Zakaria, Noor Ayuni and Johar, Khairunnisa and Othman, Nurul Atiqah and Hock Hung Chieng, Hock Hung Chieng and Hanapiah, Fazah Akhtar (2023) Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision. Diagnostics, 13 (739). pp. 1-31. https://doi.org/10.3390/diagnostics13040739 |
| spellingShingle | T Technology (General) Jingye Yee, Jingye Yee Cheng Yee Low, Cheng Yee Low Mohamad Hashim, Natiara Che Zakaria, Noor Ayuni Johar, Khairunnisa Othman, Nurul Atiqah Hock Hung Chieng, Hock Hung Chieng Hanapiah, Fazah Akhtar Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision |
| title | Clinical Spasticity Assessment Assisted by Machine Learning
Methods and Rule-Based Decision |
| title_full | Clinical Spasticity Assessment Assisted by Machine Learning
Methods and Rule-Based Decision |
| title_fullStr | Clinical Spasticity Assessment Assisted by Machine Learning
Methods and Rule-Based Decision |
| title_full_unstemmed | Clinical Spasticity Assessment Assisted by Machine Learning
Methods and Rule-Based Decision |
| title_short | Clinical Spasticity Assessment Assisted by Machine Learning
Methods and Rule-Based Decision |
| title_sort | clinical spasticity assessment assisted by machine learning methods and rule based decision |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/9822/1/J15866_3962a9a5eedc50a7d3cf3ed014298823.pdf |
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