@inproceedings{fcf6f78edcf3463eb0e62170fce64154,
title = "Improving gesture recognition by bidirectional temporal convolutional networks",
abstract = "Surface electromyography (sEMG) based gesture recognition as an important role in Muscle-Computer interface has been researched for decades. Recently, deep learning based method has had a profound impact on this field. CNN, RNN and RNN-CNN based methods were studied by many researchers. Motivated by Bidirectional Long short-term memory (Bi-LSTM) and Temporal Convolutional Networks (TCN), we propose 1D CNN based networks called Bidirectional Temporal Convolutional Networks (Bi-TCN). The positive order signal and reverse order sEMG signal are feed to our networks to learn the different representation of the same sEMG signal. We evaluate proposed networks on two benchmark datasets, Ninapro DB1 and DB5. Our networks yields 90.74\% prediction accuracy on DB1 and 90.06\% prediction accuracy on DB5. The results demonstrate our networks is comparable to the state-of-the-art works.",
keywords = "sEMG, deep learning, CNN, TCN, LSTM, gesture recognition",
author = "Haoyu Chen and Yue Zhang and Dalin Zhou and Honghai Liu",
year = "2021",
month = jan,
day = "3",
doi = "10.1007/978-981-33-4932-2\_30",
language = "English",
isbn = "978-981-33-4931-5",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "413--424",
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",
}