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

Authors

  • Muhammad Awais Khan Department of Mechatronics Engineering, University of Engineering and Technology, Peshawar, Pakistan
  • Muhammad Usman Qadir Department of Mechatronics Engineering, University of Engineering and Technology, Peshawar, Pakistan
  • Izhar Ul Haq Department of Mechatronics Engineering, University of Engineering and Technology, Peshawar, Pakistan

DOI:

https://doi.org/10.51846/vol6iss3pp44-50

Keywords:

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

Abstract

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

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Published

2023-12-31

How to Cite

[1]
M. A. Khan, M. U. Qadir, and I. U. Haq, “Machine Learning-Based Gait Phase Detection for Semi-Active Prosthetic Knee”, PakJET, vol. 6, no. 3, pp. 44–50, Dec. 2023.

Issue

Vol. 6 No. 3 (2023): Pakistan Journal of Engineering and Technology

Section

Research Articles

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