TY - JOUR
T1 - Evolutionary Optimization Of Ground Reaction Force For A Prosthetic Leg Testing Robot
AU - Davis, Ron
AU - Richter, Hanz
AU - Simon, Dan
AU - van den Bogert, Antonie
AU - Simon, Daniel J.
N1 - R. Davis, H. Richter, D. Simon and A. van den Bogert, "Evolutionary optimization of ground reaction force for a prosthetic leg testing robot," in 2014 American Control Conference, 2014, pp. 4081-4086.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Transfemoral amputees modify their gait in order to compensate for their prosthetic leg. This compensation causes harmful secondary physical conditions due to an over-dependence on the intact limb and deficiencies of the prosthesis. Even with more advanced prostheses, amputees still have to alter their gait to compensate for the prosthesis. We present a novel way to quantify how much an amputee has to compensate for a prosthetic leg. We train a newly-developed prosthetic leg testing robot to walk with a prosthesis using an evolutionary algorithm called biogeography-based optimization (BBO). The robot is initially commanded to follow able-bodied hip and thigh trajectories, and BBO then modifies these reference inputs. We adjust the reference inputs to minimize the error between the ground reaction force (GRF) of able-bodied gait data, and that of the robot while walking with the prosthesis. Experimental results show a 62% decrease in the GRF error, effectively demonstrating the robot's compensation for the prosthesis.
AB - Transfemoral amputees modify their gait in order to compensate for their prosthetic leg. This compensation causes harmful secondary physical conditions due to an over-dependence on the intact limb and deficiencies of the prosthesis. Even with more advanced prostheses, amputees still have to alter their gait to compensate for the prosthesis. We present a novel way to quantify how much an amputee has to compensate for a prosthetic leg. We train a newly-developed prosthetic leg testing robot to walk with a prosthesis using an evolutionary algorithm called biogeography-based optimization (BBO). The robot is initially commanded to follow able-bodied hip and thigh trajectories, and BBO then modifies these reference inputs. We adjust the reference inputs to minimize the error between the ground reaction force (GRF) of able-bodied gait data, and that of the robot while walking with the prosthesis. Experimental results show a 62% decrease in the GRF error, effectively demonstrating the robot's compensation for the prosthesis.
KW - Biomedical
KW - Evolutionary computing
KW - Mechatronics
UR - https://engagedscholarship.csuohio.edu/enme_facpub/358
UR - https://ieeexplore.ieee.org/document/6858812
UR - https://engagedscholarship.csuohio.edu/enece_facpub/347
UR - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6858812abstractAccess=nouserType=inst
U2 - 10.1109/ACC.2014.6858812
DO - 10.1109/ACC.2014.6858812
M3 - Article
JO - 2014 American Control Conference (ACC)
JF - 2014 American Control Conference (ACC)
ER -