Gesture Recognition Using MLP-Mixer With CNN and Stacking Ensemble for sEMG Signals

Shen, Shu; Li, Minglei; Mao, Fan; Chen, Xinrong; Ran, Ran

doi:10.1109/jsen.2023.3347529

Cited by 5 publications

(1 citation statement)

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“…As the number of recognized hand gestures increases, the recognition accuracy tends to decrease, mainly due to the selection of rather limited features. Therefore, expanding the range of feature selection and using deep learning to replace traditional classification methods, such as Support Vector Machines (SVM) or neural networks, can significantly enhance the recognition rate [8,9]. These studies highlight the potential of deep learning in improving recognition accuracy and the need to expand feature selection to accommodate complex gesture recognition.…”

Section: Introductionmentioning

confidence: 99%

Mapping Method of Human Arm Motion Based on Surface Electromyography Signals

Zheng,

Zhang

et al. 2024

Sensors

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This paper investigates a method for precise mapping of human arm movements using sEMG signals. A multi-channel approach captures the sEMG signals, which, combined with the accurately calculated joint angles from an Inertial Measurement Unit, allows for action recognition and mapping through deep learning algorithms. Firstly, signal acquisition and processing were carried out, which involved acquiring data from various movements (hand gestures, single-degree-of-freedom joint movements, and continuous joint actions) and sensor placement. Then, interference signals were filtered out through filters, and the signals were preprocessed using normalization and moving averages to obtain sEMG signals with obvious features. Additionally, this paper constructs a hybrid network model, combining Convolutional Neural Networks and Artificial Neural Networks, and employs a multi-feature fusion algorithm to enhance the accuracy of gesture recognition. Furthermore, a nonlinear fitting between sEMG signals and joint angles was established based on a backpropagation neural network, incorporating momentum term and adaptive learning rate adjustments. Finally, based on the gesture recognition and joint angle prediction model, prosthetic arm control experiments were conducted, achieving highly accurate arm movement prediction and execution. This paper not only validates the potential application of sEMG signals in the precise control of robotic arms but also lays a solid foundation for the development of more intuitive and responsive prostheses and assistive devices.

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Section: Introductionmentioning

confidence: 99%