Longitudinal Evaluation of Diadochokinesia Characteristics for Hemiplegic Ankle Rehabilitation by Wearable Systems with Machine Learning

The ability to objectively evaluate the efficacy of a rehabilitation regimen from a longitudinal perspective is significant and enabled through the confluence of wearable and wireless inertial sensor systems and machine learning. These capabilities are demonstrated in the context of a 10 month longitudinal study involving a rehabilitation regimen for improving a hemiplegic ankle, such as with respect to the hemiplegic ankle's diadochokinesia characteristics. Diadochokinesia represents the ability to alternate between agonist and antagonist muscles, such as the dorsiflexion and plantar flexion musculature. The ability to smoothly transition between dorsiflexion and plantar flexion musculature is inherent for the rhythmic process of gait. Using a smartphone as a functional wearable gyroscope platform secured to the dorsum of the foot by an armband, the kinematic properties of the hemiplegic ankle are quantified and recorded in a longitudinal context. A support vector machine implemented through the Waikato Environment for Knowledge Analysis (WEKA) machine learning platform successfully distinguished between the initial phase and final phase of a 10 month longitudinal study involving a rehabilitation regimen for a hemiplegic ankle with considerable classification accuracy. The implications of the research findings establish a pathway for the ascertaining the efficacy of a rehabilitation regimen based on the signal data acquired by wearable systems in conjunction with machine learning.