Quantifying spasticity: developing a data-driven approach through the modified Ashworth scale and simulated spasticity model / Nurul Atiqah Othman ... [et al.]
This study addresses the need for a quantitative assessment tool for spasticity, a common motor disorder in neurological conditions. The Simulated Spasticity Model (SSM) is developed to represent spasticity characteristics across different Modified Ashworth Scale (MAS) levels. This mathematical mode...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
UiTM Press
2025
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/110093/1/110093.pdf https://ir.uitm.edu.my/id/eprint/110093/ |
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| Summary: | This study addresses the need for a quantitative assessment tool for spasticity, a common motor disorder in neurological conditions. The Simulated Spasticity Model (SSM) is developed to represent spasticity characteristics across different Modified Ashworth Scale (MAS) levels. This mathematical model captures the spasticity behaviour, offering detailed insights that qualitative descriptions cannot provide. Ethic approval was secured, and 114 data sets met the inclusion criteria. Research hypotheses, based on MAS descriptions, focused on muscle tone progression and catch positions during passive stretching. Data underwent segmentation, cleaning, and filtering, with feature extraction for crucial information. Slow passive stretch analysis revealed a quadratic characterizing range of motion (ROM) for Malaysians, exhibiting a high R2 result of 97.36%. The fast passive stretch analysis utilized the Bi-Gaussian Peak function, creating the SSM for simplified MAS interpretation. Validation showed a significant portion of data points falling within the 0.8 to 1.0 R2 range, confirming strong alignment with the model. Results robustly supported hypotheses, confirming the expected hierarchy of initial forces, catch positions, and graph widths. This research demonstrates that MAS can be effectively represented and understood using the SSM, bridging the qualitative-quantitative gap in spasticity assessment. In conclusion, this study transforms MAS into a data-driven tool, providing a valuable contribution to spasticity education. |
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