🐙 Public Code and Data

Explore the LDA-KM-DA project focused on Non-conservative Unsupervised Domain Adaptation.

Complete framework for synchronized EMG and finger joint-angle acquisition, with temporal Riemannian regression for machine learning applications.

📂 View Associated Dataset on Zenodo: https://zenodo.org/records/19453843

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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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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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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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