Data-Driven Model for Upper Limb Spasticity Detection

Abstract

Healthcare providers in the field of physical and rehabilitation medicine play a vital role to help patients suffering spasticity readapting themselves to their normal daily activities. Mathematical modeling of spasticity has the potential to avoid the issue of variability in the assessment of spasticity using the Modified Ashworth scale (MAS). In this work, an existing mathematical model for upper limb spasticity is verified using clinical data sets of upper limb spasticity collected in Malaysia at the level of MAS 1+. The data set consists of torque values measured at each elbow angle as the elbow extends from a full flexion position to a full extension position during slow and fast stretch of the forearm. The aim is to find out the capability of the mathematical model and lay a foundation for the future work on data-driven modeling of upper limb spasticity based on the Modified Ashworth Scale.