Longitudinal Evaluation of Hemiplegic Ankle Rehabilitation Efficacy by Wearable Inertial Sensor Systems with an Assortment of Machine Learning Algorithms
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.
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