Evolutionary Optimization of User Intent Recognition for Transfemoral Amputees

Document Type


Publication Date


Publication Title

Biomedical Circuits and Systems Conference (BioCAS), 2015 IEEE


Lower-limb prosthetic legs help amputees regain their walking ability. User intent recognition is utilized to infer human gait mode (fast walk, slow walk, etc.) so the controller can be adjusted depending on the detected gait mode. In this paper, mechanical sensor data is collected from an able-bodied subject and used for user intent recognition. Feature extraction, principal component analysis, correlation analysis, and K-nearest neighbor methods are used, modified, and optimized with an evolutionary algorithm for improved performance. The optimized system successfully classifies four different walking modes with an accuracy of 96%.


This research was supported by National Science Foundation Grant 1344954.

Original Citation

G. Khademi, H. Mohammadi, D. Simon and E. C. Hardin, "Evolutionary optimization of user intent recognition for transfemoral amputees," in Biomedical Circuits and Systems Conference (BioCAS), 2015 IEEE, 2015, pp. 1-4.