Creation of the website 🤠

🎉 Welcome to my website!
 I’m delighted to share with you my research work on exciting topics such as machine learning applied to neurophysiology, intelligent robotic prosthetics, and transfer learning.
Here you'll find my latest scientific publications, interactive demos, images, videos… and some personal reflections along the way!
💬 Have a question, an idea, or a collaboration in mind?
 📩 Contact me at martin.colot@ulb.be

Also, discover more work from my lab : Machine Learning Group 



Popular posts from this blog

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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