Martin Colot

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)

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