SUMMARY

Powered prosthetic technology is advancing functionality beyond what is currently capable in commercial available devices. Specifically, powered lower limb prostheses have the potential to improve community mobility, which may enhance quality of life. However, current prosthetic control strategies are not robust and there is still a lack of evidence that this robotic technology can be easily implemented in a clinical setting. The primary objective of my research is to implement and clinically-test robotic knee-ankle prosthesis control strategies that are capable of understanding user intent and performing a variety of ambulation tasks for people with transfemoral amputations. The proposed studies will focus on three key areas: 1) implement online continuous estimation of environmental variables using sensor fusion and machine learning techniques, 2) evaluate control strategies to make the human-prosthesis interaction more intuitive by involving scaling across terrains and incorporating physiological information, 3) validate prosthetic performance by comparing clinical measures to current commercially available prostheses. The study’s findings will provide valuable information for future robotic technology to improve user mobility as well as provide better user functionality with a powered device.