Author(s)

Robert LeMoyne^1,2, Timothy Mastroianni³

¹Department of Biological Sciences, Northern Arizona University, Flagstaff, USA.
²Department of Biomedical Technology, Czech Technical University in Prague, Kladno, Czech Republic.
³Cognition Engineering, Pittsburgh, USA.

With the amalgamation of wearable systems equipped with inertial sensors, such as a gyroscope, and machine learning a therapy regimen can be objectively quantified, and then the initial phase and final phase of a one year therapy regimen can be distinguished through machine learning. In the context of rehabilitation of a hemiplegic ankle, a longitudinal therapy regimen incorporating stretching and then a series of repetitions for raising and lowering the foot of the hemiplegic ankle can be applied over the course of a year. Using a smartphone equipped with an application to function as a wearable and wireless gyroscope platform mounted to the dorsum of the foot by an armband, the initial phase and final phase of a one year longitudinally applied therapy regimen can be objectively quantified and recorded for subsequent machine learning. Considerable classification accuracy is attained to distinguish between the initial phase and final phase by a support vector machine for a one year longitudinally applied hemiplegic ankle therapy regimen based on the gyroscope signal data obtained by a smartphone functioning as a wearable and wireless inertial sensor system.

Smartphone, Gyroscope, Machine Learning, Hemiparesis, Rehabilitation, Ankle, Longitudinal Evaluation

LeMoyne, R. and Mastroianni, T. (2023) Longitudinal Evaluation of Hemiplegic Ankle Rehabilitation Efficacy by Wearable Inertial Sensor Systems with an Assortment of Machine Learning Algorithms. Journal of Biomedical Science and Engineering, 16, 121-131. doi: 10.4236/jbise.2023.169009.