TY - GEN
T1 - Hand tracking accuracy enhancement by data fusion using leap motion and myo armband
AU - Chen, Jingxiang
AU - Liu, Chao
AU - Cui, Rongxin
AU - Yang, Chenguang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - In this paper, by using the combination of Leap Motion and Myo armband, two methods for hand tracking and online hand gesture identification are proposed. With the proposed methods, We have improved the measurement accuracy of the palm direction and solved the problem of insufficient accuracy when the palm is at the limit of the measurement range. We use the Kalman filter algorithm and the neural network classification method to process the data measured by Leap Motion and Myo, so that the tracking of the operator's hand gesture is more accurate and robust even when the hand is at positions close to the measurement limit of one single sensor. The methods, which improve the hand tracking accuracy, can be used for robotic control, demonstration or teleoperation. The effectiveness of the proposed methods has been demonstrated through comparative experiments.
AB - In this paper, by using the combination of Leap Motion and Myo armband, two methods for hand tracking and online hand gesture identification are proposed. With the proposed methods, We have improved the measurement accuracy of the palm direction and solved the problem of insufficient accuracy when the palm is at the limit of the measurement range. We use the Kalman filter algorithm and the neural network classification method to process the data measured by Leap Motion and Myo, so that the tracking of the operator's hand gesture is more accurate and robust even when the hand is at positions close to the measurement limit of one single sensor. The methods, which improve the hand tracking accuracy, can be used for robotic control, demonstration or teleoperation. The effectiveness of the proposed methods has been demonstrated through comparative experiments.
KW - Hand guesture identification
KW - Hand tracking
KW - Leap Motion
KW - Myo
KW - Sensor fusion
UR - http://www.scopus.com/inward/record.url?scp=85087908730&partnerID=8YFLogxK
U2 - 10.1109/ICUSAI47366.2019.9124812
DO - 10.1109/ICUSAI47366.2019.9124812
M3 - 会议稿件
AN - SCOPUS:85087908730
T3 - 2019 IEEE International Conference on Unmanned Systems and Artificial Intelligence, ICUSAI 2019
SP - 256
EP - 261
BT - 2019 IEEE International Conference on Unmanned Systems and Artificial Intelligence, ICUSAI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Unmanned Systems and Artificial Intelligence, ICUSAI 2019
Y2 - 22 November 2019 through 24 November 2019
ER -