Quantifying Spasticity: Developing a Data-Driven Approach Through the Modified Ashworth Scale and Simulated Spasticity Model

• Published in: Journal of Mechanical Engineering
• Main Author: Othman N.A.; Zakaria N.A.C.; Johar K.; Hanapiah F.A.; Hashim N.M.; Low C.Y.; Yee J.
• Format: Article
• Language: English
• Published: UiTM Press 2025
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215668010&doi=10.24191%2fjmeche.v22i1.4560&partnerID=40&md5=435f94fa1c44d3ee4afb54a8f0a0bb0c

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. © (2024), (UiTM Press). All Rights Reserved.