TY - JOUR
T1 - Wearer-Prosthesis Interaction for Symmetrical Gait
T2 - A Study Enabled by Reinforcement Learning Prosthesis Control
AU - Wen, Yue
AU - Li, Minhan
AU - Si, Jennie
AU - Huang, He
N1 - Funding Information:
Manuscript received October 14, 2019; revised December 21, 2019 and January 24, 2020; accepted February 25, 2020. Date of publication March 9, 2020; date of current version April 8, 2020. This work was supported in part by the National Science Foundation under Grant 1563454, Grant 1563921, Grant 1808752, Grant 1808898, and Grant NIH EB024570. (Corresponding authors: Jennie Si; He Huang.) Yue Wen, Minhan Li, and He Huang are with the UNC/NC State Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC 27695 USA, and also with the UNC/NC State Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA (e-mail: hhuang11@ncsu.edu).
Publisher Copyright:
© 2001-2011 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - With advances in robotic prostheses, rese-archers attempt to improve amputee's gait performance (e.g., gait symmetry) beyond restoring normative knee kinematics/kinetics. Yet, little is known about how the prosthesis mechanics/control influence wearer-prosthesis' gait performance, such as gait symmetry, stability, etc. This study aimed to investigate the influence of robotic transfemoral prosthesis mechanics on human wearers' gait symmetry. The investigation was enabled by our previously designed reinforcement learning (RL) supplementary control, which simultaneously tuned 12 control parameters that determined the prosthesis mechanics throughout a gait cycle. The RL control design facilitated safe explorations of prosthesis mechanics with the human in the loop. Subjects were recruited and walked with a robotic transfemoral prosthesis on a treadmill while the RL controller tuned the control parameters. Stance time symmetry, step length symmetry, and bilateral anteroposterior (AP) impulses were measured. The data analysis showed that changes in robotic knee mechanics led to movement variations in both lower limbs and therefore gait temporal-spatial symmetry measures. Consistent across all the subjects, inter-limb AP impulse measurements explained gait symmetry: the stance time symmetry was significantly correlated with the net inter-limb AP impulse, and the step length symmetry was significantly correlated with braking and propulsive impulse symmetry. The results suggest that it is possible to personalize transfemoral prosthesis control for improved temporal-spatial gait symmetry. However, adjusting prosthesis mechanics alone was insufficient to maximize the gait symmetry. Rather, achieving gait symmetry may require coordination between the wearer's motor control of the intact limb and adaptive control of the prosthetic joints. The results also indicated that the RL-based prosthesis tuning system was a potential tool for studying wearer-prosthesis interactions.
AB - With advances in robotic prostheses, rese-archers attempt to improve amputee's gait performance (e.g., gait symmetry) beyond restoring normative knee kinematics/kinetics. Yet, little is known about how the prosthesis mechanics/control influence wearer-prosthesis' gait performance, such as gait symmetry, stability, etc. This study aimed to investigate the influence of robotic transfemoral prosthesis mechanics on human wearers' gait symmetry. The investigation was enabled by our previously designed reinforcement learning (RL) supplementary control, which simultaneously tuned 12 control parameters that determined the prosthesis mechanics throughout a gait cycle. The RL control design facilitated safe explorations of prosthesis mechanics with the human in the loop. Subjects were recruited and walked with a robotic transfemoral prosthesis on a treadmill while the RL controller tuned the control parameters. Stance time symmetry, step length symmetry, and bilateral anteroposterior (AP) impulses were measured. The data analysis showed that changes in robotic knee mechanics led to movement variations in both lower limbs and therefore gait temporal-spatial symmetry measures. Consistent across all the subjects, inter-limb AP impulse measurements explained gait symmetry: the stance time symmetry was significantly correlated with the net inter-limb AP impulse, and the step length symmetry was significantly correlated with braking and propulsive impulse symmetry. The results suggest that it is possible to personalize transfemoral prosthesis control for improved temporal-spatial gait symmetry. However, adjusting prosthesis mechanics alone was insufficient to maximize the gait symmetry. Rather, achieving gait symmetry may require coordination between the wearer's motor control of the intact limb and adaptive control of the prosthetic joints. The results also indicated that the RL-based prosthesis tuning system was a potential tool for studying wearer-prosthesis interactions.
KW - Wearer-prosthesis interaction
KW - anteroposterior impulse
KW - gait asymmetry
KW - reinforcement learning
KW - robotic knee prosthesis
UR - http://www.scopus.com/inward/record.url?scp=85083163810&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083163810&partnerID=8YFLogxK
U2 - 10.1109/TNSRE.2020.2979033
DO - 10.1109/TNSRE.2020.2979033
M3 - Article
C2 - 32149646
AN - SCOPUS:85083163810
SN - 1534-4320
VL - 28
SP - 904
EP - 913
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
IS - 4
M1 - 9028255
ER -