Optimization of inter-subject sEMG-based hand gesture recognition tasks using unsupervised domain adaptation techniques

Wang, Zihao; Wan, Huiying; Meng, Long; Zeng, Zheng; Akay, Metin; Chen, Chen; Chen, Wei

doi:10.1016/j.bspc.2024.106086

Biomedical Signal Processing and Control

2024

DOI: 10.1016/j.bspc.2024.106086

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Optimization of inter-subject sEMG-based hand gesture recognition tasks using unsupervised domain adaptation techniques

Zihao Wang,

Huiying Wan,

Long Meng

et al.

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2024

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Cited by 4 publications

References 17 publications

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Unsupervised neural decoding for concurrent and continuous multi-finger force prediction

Meng,

2024

Computers in Biology and Medicine

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Unsupervised neural decoding for concurrent and continuous multi-finger force prediction

Meng,

2024

Computers in Biology and Medicine

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A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities

Shin,

Miah,

Kabir

et al. 2024

IEEE Access

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EMG-based Multi-User Hand Gesture Classification via Unsupervised Transfer Learning Using Unknown Calibration Gestures

Shi,

Jiang,

Dai

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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The poor generalization performance and heavy training burden of the gesture classification model contribute as two main barriers that hinder the commercialization of sEMG-based human-machine interaction (HMI) systems. To overcome these challenges, eight unsupervised transfer learning (TL) algorithms developed on the basis of convolutional neural networks (CNNs) were explored and compared on a dataset consisting of 10 gestures from 35 subjects. The highest classification accuracy obtained by CORrelation Alignment (CORAL) reaches more than 90%, which is 10% higher than the methods without using TL. In addition, the proposed model outperforms 4 common traditional classifiers (KNN, LDA, SVM, and Random Forest) using the minimal calibration data (two repeated trials for each gesture). The results also demonstrate the model has a great transfer robustness/flexibility for cross-gesture and cross-day scenarios, with an accuracy of 87.94% achieved using calibration gestures that are different with model training, and an accuracy of 84.26% achieved using calibration data collected on a different day, respectively. As the outcomes confirm, the proposed CNN TL method provides a practical solution for freeing new users from the complicated acquisition paradigm in the calibration process before using sEMG-based HMI systems.

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Optimization of inter-subject sEMG-based hand gesture recognition tasks using unsupervised domain adaptation techniques

Cited by 4 publications

References 17 publications

Unsupervised neural decoding for concurrent and continuous multi-finger force prediction

Unsupervised neural decoding for concurrent and continuous multi-finger force prediction

A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities

EMG-based Multi-User Hand Gesture Classification via Unsupervised Transfer Learning Using Unknown Calibration Gestures

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