期刊
IEEE INTERNET OF THINGS JOURNAL
卷 7, 期 8, 页码 6979-6992出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2020.2979328
关键词
Electrodes; Gesture recognition; Muscles; Electromyography; Internet of Things; Empirical mode decomposition; Convolutional recurrent neural network (CRNN); gesture recognition; multivariate empirical mode decomposition (MEMD); surface electromyography (sEMG)
资金
- National Key Research and Development Plan of China [2017YFB1002801]
- Natural Science Foundation of China [61972383, 61502456]
- Research and Development Plan in Key Field of Guangdong Province [2019B010109001]
- Alibaba Group through the Alibaba Innovative Research Program
Surface electromyography (sEMG) armband-based gesture recognition is an active research topic that aims to identify hand gestures with a single row of sEMG electrodes. As a typical type of biological signal, sEMG on one channel is nonstationary temporally and related to multiple adjacent muscles spatially, which hinders the effective representation in gesture recognition. To tackle these aspects, we propose a spatial-temporal features-based gesture recognition method (STF-GR) in this article. Specifically, STF-GR first decomposes the nonstationary multichannel sEMG by multivariate empirical mode decomposition, which jointly transforms each channel into a series of stationary subsignals. It can keep the temporal stationarity within-channel as well as the spatial independence across-channel. Then, by the convolutional recurrent neural network, STF-GR extracts and merges spatial-temporal features of decomposed sEMG signal. Finally, a negative log-likelihood-based cost function is used to make the final gesture decision. To evaluate the performance of STF-GR, we conduct experiments on three data sets, noninvasive adaptive hand prosthetic (NinaPro), CapgMyo, and BandMyo. The first two are publicly available, and BandMyo is collected by ourselves. Experimental evaluations with within-subject tests show that STF-GR exceeds the performance of other state-of-the-art methods, including deep learning algorithms that are not focused on spatial-temporal features and traditional machine learning algorithms that use handcrafted features.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据