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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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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