BP Neural Network Based On-board Training for Real-time Locomotion Mode Recognition in Robotic Transtibial Prostheses

Abstract

Locomotion mode recognition based on the off-line trained model brings difficulties in integration and application to wearable robots. In this paper, we put forward an on-board training based on back propagation (BP) neural network and developed the real-time locomotion mode recognition research in robotic transtibial prosthesis. Three transtibial amputees participated in the study to finish the designed six experimental tasks (standing, level ground walking, stair ascending and descending, ramp ascending and descending) with robotic transtibial prostheses. Data of six locomotion modes were collected under normal speed condition as training data set to train model on board. Based on the on-board trained models, real-time recognition experiments were developed under three different speeds conditions. The total recognition accuracies were 91.54%, 96.72% and 95.35% corresponding to slow, normal and fast speeds, respectively. The results showed some adaptation of recognition for the six locomotion modes at different speeds. The on-board training strategy was feasible and effective with satisfactory performance.