Wearable spasticity estimation and validation using machine learning

• Published in: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) pp. 2109 - 2112
• Main Authors: Wang, Shou-Jen, Park, Jeong-Ho, Park, Hyung-Soon, Nanda, Devak, Albert, Mark V.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.12.2020
• Subjects: Correlation; Data models; Random forests; Regression tree analysis; Reliability; Sensors; Support vector machines
• DOI: 10.1109/BIBM49941.2020.9313130

The Modified Ashworth Scale (MAS) is one of the most commonly used classification metrics for spasticity. While MAS is easy to implement, it relies on the subjective judgement of the clinician which leads to the poor inter-rater reliability between clinicians. We developed a device worn over the arm which measures angular velocity, angular acceleration, resistance, mechanical power, and elbow extension angle of the elbow joint. This device was used on 32 subjects who have spasticity in the upper forearm by 29 clinicians who evaluated the subjects, yielding a data set of 634 evaluations. Predictive models were developed including linear, decision tree, random forest, and support vector machines using variants of the models for regression and classification. Model hyperparameter tuning and testing was performed using clinician-wise nested cross-validation. The support vector classifier model was found to have the best root mean square error of 0.4449. Using a trained models can help eliminate much of the known variability in the MAS. By creating a more accurate way of assessing spasticity, better treatment plans can be more readily compared to aid in the treatment of spasticity and improve their quality of life.