🎓 My PhD

Thesis Title: Transfer Learning for Cross-Subject Hand Gesture Recognition From EMG

Supervisors: Gianluca Bontempi and Ana Maria Cebolla Alvarez

Labs: Machine Learning Group and LNMB

Main collaborators: Cédric Simar, Mathieu Petieau, Guy Chéron, Ana Maria Cebolla Alvarez, and Gianluca Bontempi

I started my PhD in October 2022, with a 6-year teaching assistant funding at the département d'informatique of the Université Libre de Bruxelles.

Main Projects

  • Creating a dataset for hand gesture classification from EMG ✅
  • Evaluating EMG classification models in intra-subject and cross-subject ✅
  • Designing an Unsupervised Adaptation method for cross-subject EMG hand gesture classification ✅
  • Evaluating the adaptation method with offline datasets against the state-of-the-art ✅
  • Evaluating the adaptation method with online feedback 🔨
  • Collecting data for regression of finger joint angles during complex gestures 🔨
  • Designing a regression model of finger joint angles 🔨
  • Adapting the Adaptation method to work with regression data ⌛
  • Performing the gestures recognized by an EMG model on a robotic hand ⌛

Teaching at ULB (practical courses and projects)

INFO-F103 Algorithmique 2022 - today
INFO-F204 Analyse et Méthodes 2022 - today
INFO-F206 Informatique 2022 - 2025
INFO-F307 Génie Logiciel et Gestion de Projects 2022 - today
INFO-H515 Big Data, Distributed Management, and Scalable Analytics 2023 - today
Organisation of the MLG student day (Master thesis presentation) 2022 - today

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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