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

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
Domain Adaptation Visual Results
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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