TY - GEN
T1 - Robust EMG pattern recognition with electrode donning/doffing and multiple confounding factors
AU - Zhang, Huajie
AU - Yang, Dapeng
AU - Shi, Chunyuan
AU - Jiang, Li
AU - Liu, Hong
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Traditional electromyography (EMG) pattern recognition did not take into account confounding factors such as electrode shifting, force variation, limb posture, etc., which lead to a great gap between academic research and clinical practice. In this paper, we investigated the robustness of EMG pattern recognition under conditions of electrode shifting, force varying, limb posture changing, and dominant/non-dominant hand switching. In feature extraction, we proposed a method for threshold optimization based on Particle Swarm Optimization (PSO). Compared with the traditional trail & error method, it can largely increase the classification accuracy (CA) by 10.2%. In addition, the hybrid features integrated with discrete Fourier transform (DFT), wavelet transform (WT), and wavelet packet transform (WPT) were proposed, which increased the CA by 30.5%, 25.4%, 22.9%, respectively. We introduced probabilistic neural network (PNN) as a new classifier for EMG pattern recognition, and reported the CA’s obtained by a large variety of features and classifiers. The results showed that the combination of DFT_MAV2 (a novel feature based on DFT) and PNN reached the best CA (45.5%, 14 motions, validated on different hands without re-training).
AB - Traditional electromyography (EMG) pattern recognition did not take into account confounding factors such as electrode shifting, force variation, limb posture, etc., which lead to a great gap between academic research and clinical practice. In this paper, we investigated the robustness of EMG pattern recognition under conditions of electrode shifting, force varying, limb posture changing, and dominant/non-dominant hand switching. In feature extraction, we proposed a method for threshold optimization based on Particle Swarm Optimization (PSO). Compared with the traditional trail & error method, it can largely increase the classification accuracy (CA) by 10.2%. In addition, the hybrid features integrated with discrete Fourier transform (DFT), wavelet transform (WT), and wavelet packet transform (WPT) were proposed, which increased the CA by 30.5%, 25.4%, 22.9%, respectively. We introduced probabilistic neural network (PNN) as a new classifier for EMG pattern recognition, and reported the CA’s obtained by a large variety of features and classifiers. The results showed that the combination of DFT_MAV2 (a novel feature based on DFT) and PNN reached the best CA (45.5%, 14 motions, validated on different hands without re-training).
KW - Dynamic limb posture
KW - Electrode shifting
KW - Feature extraction
KW - Myoelectric signal
KW - Pattern recognition
UR - https://www.scopus.com/pages/publications/85028343573
U2 - 10.1007/978-3-319-65298-6_38
DO - 10.1007/978-3-319-65298-6_38
M3 - 会议稿件
AN - SCOPUS:85028343573
SN - 9783319652979
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 413
EP - 424
BT - Intelligent Robotics and Applications - 10th International Conference, ICIRA 2017, Proceedings
A2 - Liu, Honghai
A2 - Huang, YongAn
A2 - Wu, Hao
A2 - Yin, Zhouping
PB - Springer Verlag
T2 - 10th International Conference on Intelligent Robotics and Applications, ICIRA 2017
Y2 - 16 August 2017 through 18 August 2017
ER -