Machine Learning-Based Gait Phase Detection for Semi-Active Prosthetic Knee

Abstract

Human knee plays a vital role in performing day-to-day activities. For healthy person it is easy to perform locomotion activities but for people with transfemoral amputation, it is very difficult task. To overcome this issue prosthetic knees are developed. These prosthetic knees provide necessary function of gait cycle. In order to mimic gait cycle of human knee, it is very crucial to detect different gait phases in gait cycle. Mechanical sensors such as force and angle sensors are used to collect kinematic data and then with heuristic rule base system, the gait phases are detected. Rule-based system performs well but as number of gait phases are increased then it is difficult to identify them. This paper proposed machine learning based gait phase detection. Decision Trees, Liner discriminant analysis and Support Vector Machine, are applied to the kinematics data obtained from strain gauges and angle encoder. These algorithms are easy to implement on embedded hardware as they use low computational power. The Linear Discriminant analysis has highest validation accuracy of 95.6% and test accuracy of 95.40% while both Support Vector Machine and Decision Trees algorithm has 95.2% validation accuracy. The test accuracy of Support Vector Machine is 95.10% and for Decision Tree it is 95.05%.

Keywords

• Gait Phase Detection
• Semi Active Prosthetic Knee
• Decision Trees
• Support Vector Machine
• Linear Discriminant Analysis