Supervisory control and intent recognition of a powered knee and ankle prosthesis

Abstract

This work describes a control architecture and intent recognition approach for the real-time supervisory control of a powered lower limb prosthesis. The proposed approach infers user intent to stand, sit, or walk, by recognizing patterns in prosthesis sensor data in real-time, without the need for instrumentation of the sound-side leg. Specifically, the intent recognizer utilizes time-based features extracted from frames of prosthesis signals, which are subsequently reduced to a lower dimensionality (for computational efficiency). These data are initially used to train intent models, which classify the patterns as standing, sitting, or walking. The trained models are subsequently used to infer the user’s intent in real-time. The effectiveness of the proposed approach is demonstrated via experiments with a single unilateral amputee subject. Additionally, design of real-time slope and cadence estimators using sensors on the prosthesis is described. The extracted slope and cadence information is used to switch between different sets of controller parameters for walking on different slopes and speeds.