BioPoint: Single-site, Multi-sensor Compound Gesture Recognition
DOI:
https://doi.org/10.57922/mec.2505Abstract
EMG-based gesture recognition tasks have received a lot of attention in recent years, mostly focused on multi-channel EMG sensors, leading to issues in ease of use and computational requirements of such systems. The present study leveraged the BioPoint, a smartwatch-like device, to proceed to multi-sensor deep-learning based gesture recognition. 3 hand gestures in 3 wrist orientations were targeted by measuring the EMG, PPG and IMU waveforms on able-bodied subjects (n=10). Preprocessing and feature extraction allowed the modalities to be used in a two-head neural network trained for the simultaneous classification of hand gestures and wrist rotation. During evaluation, the model obtained an average classification accuracy for hand gestures of 83.5 ± 12.4% and 94.3 ± 9.7% for wrist position. Overall, this study showed the potential of single-site, multi-sensor approaches for compound gesture recognition.
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Published
2024-08-15
How to Cite
[1]
F. Chamberland, “BioPoint: Single-site, Multi-sensor Compound Gesture Recognition”, MEC Symposium, Aug. 2024.
Conference Proceedings Volume
Section
Myoelectric Control Algorithms
