Abstract:

In this article, a new optimization system that uses few features to recognize locomotion with high classifcation accuracy is proposed. The optimization system consists of three parts. First, the features of the mixed mechanical signal data are extracted from each analysis window of 200 ms after each foot contact event. This classifer has the advantages of high accuracy, few parameters as well as low memory burden. Based on data from eight patients with transfemoral amputations, the optimization system is evaluated. The numerical results indicate that the proposed model can recognize nine daily locomotion modes (i.e., low-, mid-, and fast-speed level-ground walking, ramp ascent/decent, stair ascent/descent, and sit/ stand) by only seven features, with an accuracy of 96.66%0.68%. As for real-time prediction on a powered knee prosthesis, the shortest prediction time is only 9.8 ms. These promising results reveal the potential of intention recognition based on the proposed system for high-level control of the prosthetic knee.

Key words: Intent recognition, K-Nearest Neighbor algorithm, Powered knee prosthesis, Locomotion mode classifcation