TY - GEN
T1 - Real-Time Onboard Human Motion Recognition Based on Inertial Measurement Units
AU - Liu, Xiuhua
AU - Zhou, Zhihao
AU - Wang, Qining
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/4/10
Y1 - 2019/4/10
N2 - Locomotion motion recognition is important in gait analysis and control of wearable robots to achieve smooth gait transitions. In this paper, we propose a support vector machine based locomotion intent prediction system using two Inertial Measurement Units (IMUs). The prediction system can classify locomotion modes in daily life onboard online. Two IMUs were put on the right front of thigh and shank of the subject respectively, and each of them generated three channels of angles, three channels of accelerations and three channels of angular velocities. To evaluate the performance of the system, several experiments are conducted on three able-bodied subjects for five activities including sit, sit-to-stand, stand, level-ground walking, and stand-to-sit. Average recognition accuracy is 94.25% ±0.72%. Most transitions can be detected before hand and no missed detections are observed for all the trials.
AB - Locomotion motion recognition is important in gait analysis and control of wearable robots to achieve smooth gait transitions. In this paper, we propose a support vector machine based locomotion intent prediction system using two Inertial Measurement Units (IMUs). The prediction system can classify locomotion modes in daily life onboard online. Two IMUs were put on the right front of thigh and shank of the subject respectively, and each of them generated three channels of angles, three channels of accelerations and three channels of angular velocities. To evaluate the performance of the system, several experiments are conducted on three able-bodied subjects for five activities including sit, sit-to-stand, stand, level-ground walking, and stand-to-sit. Average recognition accuracy is 94.25% ±0.72%. Most transitions can be detected before hand and no missed detections are observed for all the trials.
UR - https://www.scopus.com/pages/publications/85064974252
U2 - 10.1109/CYBER.2018.8688093
DO - 10.1109/CYBER.2018.8688093
M3 - 会议稿件
AN - SCOPUS:85064974252
T3 - 8th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, CYBER 2018
SP - 724
EP - 728
BT - 8th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, CYBER 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, CYBER 2018
Y2 - 19 July 2018 through 23 July 2018
ER -