TY - GEN
T1 - Real-Time Onboard Recognition of Gait Transitions for A Bionic Knee Exoskeleton in Transparent Mode
AU - Liu, Xiuhua
AU - Zhou, Zhihao
AU - Wang, Qining
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - To achieve smooth locomotion transitions, locomotion intent prediction is very important for the control of knee exoskeleton. In this study, we develop a multi-sensor based locomotion intent prediction system based on Support Vector Machine (SVM), which can identify the current locomotion mode (sit, sit-to-stand, stand, level-ground walking, or stand-to-sit) and detect the locomotion transition between these modes onboard online. Two IMUs are mounted on the unilateral front of thigh part and shank part of the knee exoskeleton, and each of them generates 9 channels data. To evaluate the performance of this prediction system, several experiments are conducted on five healthy subjects. Average recognition accuracy is 96.89% ± 0.23%. Most transitions can be detected before the onsets of the transitions and no missed detections are observed for all the trials of the five able-bodied subjects.
AB - To achieve smooth locomotion transitions, locomotion intent prediction is very important for the control of knee exoskeleton. In this study, we develop a multi-sensor based locomotion intent prediction system based on Support Vector Machine (SVM), which can identify the current locomotion mode (sit, sit-to-stand, stand, level-ground walking, or stand-to-sit) and detect the locomotion transition between these modes onboard online. Two IMUs are mounted on the unilateral front of thigh part and shank part of the knee exoskeleton, and each of them generates 9 channels data. To evaluate the performance of this prediction system, several experiments are conducted on five healthy subjects. Average recognition accuracy is 96.89% ± 0.23%. Most transitions can be detected before the onsets of the transitions and no missed detections are observed for all the trials of the five able-bodied subjects.
UR - https://www.scopus.com/pages/publications/85056665177
U2 - 10.1109/EMBC.2018.8512895
DO - 10.1109/EMBC.2018.8512895
M3 - 会议稿件
C2 - 30441074
AN - SCOPUS:85056665177
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3202
EP - 3205
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Y2 - 18 July 2018 through 21 July 2018
ER -