Journal Paper : Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality

EMG Dataset and Research

In this paper, Cédric and I present a novel dataset for hand gesture recognition based on EMG signals from the forearms. The dataset includes EMG recordings from both arms, along with motion capture data from 14 participants performing various tasks in virtual reality. It can be used for gesture classification in guided scenarios or for regression of finger joint angles during unrestricted interactions with the virtual environment.

EMG Dataset Overview

We also evaluate intra-subject classification models using different feature extraction methods. In particular, we investigate how physiologically informed feature extraction can improve model accuracy by separating the EMG signals into Move Command and Hold Command phases.

EMG Feature Extraction Illustration
EMG Feature Extraction Illustration
Read More Here:

Simar, C., Colot, M., Cebolla, A. M., Petieau, M., Cheron, G., & Bontempi, G.
Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality.
Frontiers in Neuroscience, 18, 1329411 (2024): https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2024.1329411/full

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