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

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: Real-Time Robotic Hand Control via sEMG-Based Pose Estimation during Unconstrained Movements

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This demo shows real-time control of a robotic hand (Leap Hand) from 8 sEMG sensors around the forearm using regression of the finger joint angles. The recognition model is first trained from 10 minutes of EMG data from the test subject, labeled using a Leap Motion camera . Figure 1. A. The MindRove 8-channel EMG armband system to record the sEMG signals. B. The Leap Motion Controller to estimate the hand poses. C. The Leap Hand robotic hand that represents the final physical effector. D. Synchronized sEMG signals with three representative finger joint angles. Watch the Demo Figure 2. Visualization of the true (black) versus predicted (blue) finger joint angles over time.
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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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