TY - GEN
T1 - Human Learning and Coordination in Lower-limb Physical Interactions
AU - Amatya, Sunny
AU - Rezayat Sorkhabadi, Seyed Mostafa
AU - Zhang, Wenlong
N1 - Funding Information:
This material is based upon work supported in part by the National Science Foundation under Grant IIS-1756031, and in part by the Arizona Department of Health Services under Grant ADHS18-198863. The authors are with The Polytechnic School, Ira A. Fulton Schools of Engineering, Arizona State University, Mesa, AZ, 85212, USA. Email: {samatya, srezayat, wenlong.zhang}@asu.edu. *Address all correspondence to this author.
Publisher Copyright:
© 2020 AACC.
PY - 2020/7
Y1 - 2020/7
N2 - This paper explores the gait learning and coordination through physical human-human interaction. The interaction and coordination are modeled as a two-step process: 1) encoding the human gait as a periodic process and 2) adjustment of the periodic gait cycle based on the external forces due to physical interactions. Three-legged walking experiments are conducted with two human dyads. Magnitude and direction of the interaction force, as well as the knee joint angles and ground reaction forces of the tied legs are collected. The knee joint trajectory of the two participants is modeled using dynamic movement primitives (DMP) coupled with force feedback though iterative learning. Gait coordination is modeled as a learning process based on kinematics from the last gait cycle and real-time interaction force feedback. The proposed method is compared with a popular baseline DMP model, which performs batch regression based on data from the previous gait cycle. The proposed model performed better in modeling one pair in the cooperative experiment compared to the baseline algorithm. The results and approaches for improving the algorithm are further discussed.
AB - This paper explores the gait learning and coordination through physical human-human interaction. The interaction and coordination are modeled as a two-step process: 1) encoding the human gait as a periodic process and 2) adjustment of the periodic gait cycle based on the external forces due to physical interactions. Three-legged walking experiments are conducted with two human dyads. Magnitude and direction of the interaction force, as well as the knee joint angles and ground reaction forces of the tied legs are collected. The knee joint trajectory of the two participants is modeled using dynamic movement primitives (DMP) coupled with force feedback though iterative learning. Gait coordination is modeled as a learning process based on kinematics from the last gait cycle and real-time interaction force feedback. The proposed method is compared with a popular baseline DMP model, which performs batch regression based on data from the previous gait cycle. The proposed model performed better in modeling one pair in the cooperative experiment compared to the baseline algorithm. The results and approaches for improving the algorithm are further discussed.
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U2 - 10.23919/ACC45564.2020.9147738
DO - 10.23919/ACC45564.2020.9147738
M3 - Conference contribution
AN - SCOPUS:85089565967
T3 - Proceedings of the American Control Conference
SP - 557
EP - 562
BT - 2020 American Control Conference, ACC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 American Control Conference, ACC 2020
Y2 - 1 July 2020 through 3 July 2020
ER -