Network Centric Therapy for Machine Learning Classification of Hemiplegic Gait through Conformal Wearable and Wireless Inertial Sensors

Abstract

The advent of the conformal wearable and wireless inertial sensor enables expanded utility for progressive evolution of gait quantification. The acquired signal data, such as through a gyroscope, can be wirelessly transmitted to a secure Cloud computing environment for subsequent post-processing. In particular, the amalgamation of machine learning classification can be applied for the distinction of hemiplegic gait in the context of the hemiplegic affected leg and the unaffected leg. The integration of these technologies enables the realization of Network Centric Therapy, for which the patient can ascertain the benefit of gait quantification through extremely lightweight conformal wearable and wireless inertial sensor systems that have profiles relative to a bandage. Furthermore, the clinical rehabilitation team can remotely instill therapy optimized with the augmented acuity of machine learning. The research endeavor successfully demonstrates considerable classification accuracy through machine learning for the differentiation of a hemiplegic affected leg and unaffected leg during gait.