Gesture recognition by instantaneous surface EMG images

By Wei-dong Geng, Yu Du, Wenguang Jin, Wentao Wei, Yu Hu, Jiaju Li. Scientific Reports, 2016

536 citations recorded by the source corpus.

Gesture recognition in non-intrusive muscle-computer interfaces is usually based on windowed descriptive and discriminatory surface electromyography (sEMG) features because the recorded amplitude of a myoelectric signal may rapidly fluctuate between voltages above and below zero. Here, we present that the patterns inside the instantaneous values of high-density sEMG enables gesture recognition to be performed merely with sEMG signals at a specific instant. We introduce the concept of an sEMG image spatially composed from high-density sEMG and verify our findings from a computational perspective with experiments on gesture recognition based on sEMG images with a classification scheme of a deep convolutional network. Without any windowed features, the resultant recognition accuracy of an 8-gesture within-subject test reached 89.3% on a single frame of sEMG image and reached 99.0% using simple majority voting over 40 frames with a 1,000 Hz sampling rate. Experiments on the recognition of 52 gestures of NinaPro database and 27 gestures of CSL-HDEMG database also validated that our approach outperforms state-of-the-arts methods. Our findings are a starting point for the development of more fluid and natural muscle-computer interfaces with very little observational latency. For example, active prostheses and exoskeletons based on high-density electrodes could be controlled with instantaneous responses.

Subfields: Biomedical Engineering, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/016149de2d84d522b9d1cd5d377109285a66d88d

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Citations in the corpus

Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning (2018, methodology): “Recently, ConvNets have started to be employed for hand gesture recognition using single array [4], [5] and matrix [25] of electrodes.”
Deep learning for healthcare applications based on physiological signals: A review (2018, background): “, 2016 [66] Gesture recognition CNN 18 subjects performing 16 gestures each recorded 10 times accuracy: 99.”
Deep Learning for EMG-based Human-Machine Interaction: A Review (2021, unknown): “To guarantee efficient real-time performance, the length of the sliding window is usually within 300 ms [58].”
Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation (2017, methodology): “We have briefly reported the average intra-session recognition accuracy on the CSL-HDEMG database in our previous study [17].”
Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network (2017, methodology): “Geng et al. (2016) employed an image-classiﬁcation framework with CNN to show that instantaneous sEMG signal obtained from high density sEMG recording (128 channels) can be a useful feature for hand movement classiﬁcation.”