📄 Scientific Publications

Colot, M., Simar, C., Cebolla Alvarez, A. M., & Bontempi, G.
Linear Non-Conservative Unsupervised Domain Adaptation for Cross-Subject EMG Gesture Recognition.
Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5099691

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Halin, A., Piérard, S., Vandeghen, R., Gérin, B., Zanella, M., Colot, M., ... & Van Droogenbroeck, M.
Physically Interpretable Probabilistic Domain Characterization.
In Proceedings of the Asian Conference on Computer Vision (pp. 15–33), 2024: https://openaccess.thecvf.com/...

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Simar, C., Colot, M., Cebolla, A. M., Petieau, M., Cheron, G., & Bontempi, G.
Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality.
Frontiers in Neuroscience, 18, 1329411 (2024): https://www.frontiersin.org/...

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Colot, M., Simar, C., Petieau, M., Cebolla Alvarez, A. M., Cheron, G., & Bontempi, G.
EMG subspace alignment and visualization for cross-subject hand gesture classification.
In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 416–423).
Cham: Springer Nature Switzerland, September 2023: https://arxiv.org/pdf/2401.05386

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Journal Paper: Linear Non-Conservative Unsupervised Domain Adaptation for Cross-Subject Emg Gesture Recognition

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In this paper, we introduce the novel LDA-KM-DA algorithm — a non-conservative unsupervised domain adaptation method designed to improve EMG-based hand gesture recognition across different subjects. The non-conservative nature of the method means the model is adapted using only samples from the test domain and initial pseudo-labels from source domains . This results in higher accuracy than comparable state-of-the-art approaches. ▶️ See it in action: Live Demo Illustration of LDA-KM-DA 📄 Read More Here: Colot, M. , Simar, C., Cebolla Alvarez, A. M., & Bontempi, G. Linear Non-Conservative Unsupervised Domain Adaptation for Cross-Subject EMG Gesture Recognition . Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5099691 FREE ACESS UNTIL SEPTEMBER 03 2025 : https://www.sciencedirect.com/science/article/pii/S1746809425007943?dgcid=author
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Video demo : Hand gesture recognition from EMG during unrestricted gestures

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This early demo shows my latest results of hand gesture recognition from 8 EMG signals and a single gyroscope . I’m using the MindRove EMG armband for data acquisition. MindRove EMG armband used in the experiment Before recording the video, I collected about 10 minutes of labeled training data using a LeapMotion camera. I then trained a machine learning model (details to be published) and ran inference without relocating the sensors. Live demo of real-time gesture prediction Prediction example (Black = Real Angle, Blue = Predicted, Red = Error)
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Video Demo : Hand Gesture Recognition from EMG with Unsupervised Domain Adaptation

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In this video, you can see a demonstration of my algorithm LDA-KM-DA , which performs non-conservative unsupervised domain adaptation to adapt an EMG-based hand gesture recognition model from a set of training subjects to a new test user. Initially, the model performs poorly. But as it processes more EMG data, it quickly learns and improves — and by the end, gesture recognition is nearly perfect . 🔗 Learn more: Full Article
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