A support vector machine approach to detect trans-tibial prosthetic misalignment using 3-Dimensional ground reaction force features: A proof of concept

Abstract

Prosthetists conventionally evaluate alignment based on visual interpretation of patients' gait, which is convenient, but largely subjective and depends on prosthetists' experience. In this paper, we explore the feasibility of using a support vector machine (SVM) approach to automatically detect misalignment of trans-tibial prostheses through ground reaction force (GRF). Alternate classification algorithms with varying kernels and feature sets were compared to assess the suitability for detection of a representative misalignment (six degrees of ankle plantar flexion) from normal alignment. A classical feature selection algorithm, Fisher Score, was further introduced to identify valuable features and reduce the dimension of feature sets. The SVMs achieved a detection accuracy of 96.67% at best within the same subject and 88.89%, respectively, for inter-subject. Combined horizontal and vertical components of GRF features provided the maximum detection accuracies. Propulsion peak force was identified as key variable of gait for misalignment prediction. As a proof of concept, the results demonstrate potential in applying this approach to detect prosthetic misalignment based on gait patterns, and is a step towards future developments of tools for early prevention of misalignment in clinical.