TY - GEN
T1 - IMU-Based Gait Phase Recognition for Stroke Survivors
T2 - 8th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, CYBER 2018
AU - Lou, Yu
AU - Wang, Rongli
AU - Mai, Jingeng
AU - Wang, Ninghua
AU - Wang, Qining
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/4/10
Y1 - 2019/4/10
N2 - In this paper, we present an Inertial Measurement Unit (IMU) based gait phase detection system for stroke survivors. The system consists of two IMUs tied to the thigh and shank respectively, for collecting acceleration and angular velocity during walking. Features are extracted using a 150ms sliding window and processed by a quadratic discriminant analysis classifier. Three stroke survivors were recruited with varying degrees of walking disability to test our system, and the experimental environment was level walking at preferred speeds. Experimental results show that the IMU-based gait phase detection system can accurately identify the swing phase and the stance phase, with a recognition accuracy higher than 97 %. Also, we figure out that the recognition results of utilizing one IMU alone is almost equal to the results of using two IMUs together. This study provides an idea for further research on wearable rehabilitation robots for stroke survivors.
AB - In this paper, we present an Inertial Measurement Unit (IMU) based gait phase detection system for stroke survivors. The system consists of two IMUs tied to the thigh and shank respectively, for collecting acceleration and angular velocity during walking. Features are extracted using a 150ms sliding window and processed by a quadratic discriminant analysis classifier. Three stroke survivors were recruited with varying degrees of walking disability to test our system, and the experimental environment was level walking at preferred speeds. Experimental results show that the IMU-based gait phase detection system can accurately identify the swing phase and the stance phase, with a recognition accuracy higher than 97 %. Also, we figure out that the recognition results of utilizing one IMU alone is almost equal to the results of using two IMUs together. This study provides an idea for further research on wearable rehabilitation robots for stroke survivors.
UR - https://www.scopus.com/pages/publications/85064992322
U2 - 10.1109/CYBER.2018.8688103
DO - 10.1109/CYBER.2018.8688103
M3 - 会议稿件
AN - SCOPUS:85064992322
T3 - 8th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, CYBER 2018
SP - 802
EP - 806
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.
Y2 - 19 July 2018 through 23 July 2018
ER -