Evolutionary Optimization of User Intent Recognition for Transfemoral Amputees
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%.
Khademi, Gholamreza; Mohammadi, Hanieh; Simon, Daniel J.; and Hardin, Elizabeth C., "Evolutionary Optimization of User Intent Recognition for Transfemoral Amputees" (2015). Electrical Engineering & Computer Science Faculty Publications. 330.
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.