sEMG-based gesture recognition becomes popular in human-computer interaction due to its non-invasive nature and ease of signal acquisition. However, electrode-shift due to long-time use or re-wearing severely decreases the gesture recognition accuracy. In this paper, we apply a LightViT network to sEMG-based gesture recognition to improve the recognition accuracy under electrode-shift conditions with economical computational complexity. Firstly, an sEMG signals database is established to simulate conditions that the electrode-band in forearm shift between 0°–45°. Secondly, a short-time Fourier transform is used to transform sEMG signals into two-dimensional images, preserving the temporal dependency and correlation of the original time series. Then, the LightViT network is applied to feature extraction and classification of two-dimensional sEMG signals. A concept of feature alignment in transfer learning is introduced by applying Triplet Loss and Focal Loss as loss functions in the model to pay more attention to complex samples. Experiment results shows that the LightViT network achieves a gesture recognition accuracy of 91.9% within a range of 0°–45° electrode-shift compared with ResNet (85.7%) and mViT (89.3%) and demonstrates fewer methods parameters and floating-point operations.
Gesture Recognition Based on LightViT Network Against sEMG Electrode Shift
Lect.Notes Computer
International Conference on Intelligent Robotics and Applications ; 2023 ; Hangzhou, China July 05, 2023 - July 07, 2023
2023-10-21
10 pages
Article/Chapter (Book)
Electronic Resource
English
Gesture Recognition , sEMG , Electrode Shift , LightViT Computer Science , Artificial Intelligence , Software Engineering/Programming and Operating Systems , Computer Applications , Computer Communication Networks , Special Purpose and Application-Based Systems , User Interfaces and Human Computer Interaction
Real-time Gesture Recognition Based on Improved Artificial Neural Network and sEMG Signals
British Library Conference Proceedings | 2021
|SEMG Based Recognition for Lumbar Muscle Fatigue During Prolonged Driving
Springer Verlag | 2012
|SEMG Based Recognition for Lumbar Muscle Fatigue During Prolonged Driving
Tema Archive | 2012
|D-S evidential theory on sEMG signal recognition
British Library Online Contents | 2017
|