A Support Vector Regression Approach for Continuous Prediction of Ankle Angle and Moment During Walking: An Implication for Developing a Control Strategy for Active Ankle Prostheses.

Abstract

Lower limb amputations impair normal locomotion. This calls for the use of prosthetic devices to restore the lost or disabled functionality. Most of the commercially available prostheses offer only passive assistance with limited capacity. On the other hand, active prostheses may better restore movement, by supporting missing muscle function with additional motor power. The control algorithms of such embedded motors must understand the users locomotive intention to produce the required locomotion similar to that of an able-bodied individual. For individuals with transtibial amputation, the control algorithm should produce the desired locomotion by controlling an active ankle joint to generate appropriate ankle angle and ankle moment. In this paper, a strategy is proposed for the continuous estimation of ankle angle and ankle moment during walking using a support vector regression approach. Experimentally obtained hip and knee joint motion data were provided as the inputs to the support vector regression model. It is shown that, for level ground walking at self-selected speed, the proposed method could predict the ankle angle and moment with high accuracy (mean R2 value of 0.98 for ankle angle and 0.97 for ankle moment).