@inproceedings{3133942fbd2942a1bf8438bd16917110,
title = "sEMG-based hand gesture classification with transient signal",
abstract = "Surface electromyography (sEMG) can provide a novel control method for human machine interface (HMI) with the improvement of signal decoding technology. sEMG-based hand gesture recognition is the key part of HMI control strategy. However, unstable and complex daily used scenarios hinder the further development of sEMG-based control strategy. In this paper, we concentrate on the data preprocessing part. Three different signal segments were extracted including the transient signal segments between gestures, standard signal segments and stationary signal segments which is smaller than standard segment. By setting up several experiments to analyze and evaluate the classification performance with these transient information. Our research found that transient signal segments can reflects more effective information than the stationary signals in inter-subject scenes. It gained more classification accuracy and stability. In addition, it also performance better in other two scenes in ten hand gesture recognition in intra-session and inter-session.",
keywords = "sEMG, feature extraction, traditional classifier, transient signal",
author = "Yue Zhang and Jiahui Yu and Dalin Zhou and Honghai Liu",
year = "2021",
month = jan,
day = "3",
doi = "10.1007/978-981-33-4932-2\_29",
language = "English",
isbn = "978-981-33-4931-5",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "401--412",
editor = "Jianhua Qian and Honghai Liu and Jiangtao Cao and Dalin Zhou",
booktitle = "ICRRI 2020: Robotics and Rehabilitation Intelligence",
address = "Netherlands",
note = "1st International Conference on Robotics and Rehabilitation Intelligence, ICRRI 2020 ; Conference date: 09-09-2020 Through 11-09-2020",
}