Implementation of machine learning for classifying prosthesis type through conventional gait analysis

Current forecasts imply a significant increase in the quantity of lower limb amputations. Synergizing the capabilities of a conventional gait analysis system and machine learning facilitates the capacity to classify disparate types of transtibial prostheses. Automated classification of prosthesis type may eventually advance rehabilitative acuity for selecting an appropriate prosthesis for a given aspect of the rehabilitation process. The presented research utilized a force plate as a conventional gait analysis device to acquire a feature set for two types of prosthesis: passive Solid Ankle Cushioned Heel (SACH) and the iWalk BiOM powered prosthesis. The feature set consists of both temporal and kinetic data with respect to the force plate signal during stance. Intuitively a passive prosthesis and powered prosthesis generate distinctively different force plate recordings. A support vector machine, which is type of machine learning application, achieves 100% classification between a passive prosthesis and powered prosthesis regarding the feature set derived from force plate recordings.