Abstract
Electromyograms (EMG)-based hand gesture recognition systems are a promising technology for human/machine interfaces. However, one of their main limitations is the long calibration time that is typically required to handle new users. The paper discusses and analyses the challenge of cross-subject generalization thanks to an original dataset containing the EMG signals of 14 human subjects during hand gestures. The experimental results show that, though an accurate generalization based on pooling multiple subjects is hardly achievable, it is possible to improve the cross-subject estimation by identifying a robust low-dimensional subspace for multiple subjects and aligning it to a target subject. A visualization of the subspace enables us to provide insights for the improvement of cross-subject generalization with EMG signals.
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Notes
- 1.
This dataset is expected to be published shortly in another paper.
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Acknowledgements
We gratefully thank all the members of the Laboratory of Neurophysiology and Movement Biomechanics (ULB) for the expertise and equipment they provided us during our data acquisition for this work.
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Colot, M., Simar, C., Petieau, M., Cebolla Alvarez, A.M., Cheron, G., Bontempi, G. (2025). EMG Subspace Alignment and Visualization for Cross-Subject Hand Gesture Classification. In: Meo, R., Silvestri, F. (eds) Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2023. Communications in Computer and Information Science, vol 2136. Springer, Cham. https://doi.org/10.1007/978-3-031-74640-6_34
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