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Related Experiment Video

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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Finger joint angle and gesture estimation under natural conditions with a soft printed electrode array.

Nitzan Luxembourg¹, Rufael Fekadu Marew², Dvir Teitelbaum¹

¹School of Electrical and Computer Engineering, Tel Aviv University, Tel Aviv, Israel.

APL Bioengineering

|October 6, 2025

Summary

Surface electromyography (sEMG) enables finger gesture recognition for human-machine interfaces. This study enhances sEMG accuracy for both static and dynamic hand gestures using a novel sensor and AI model.

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Area of Science:

Biomedical Engineering
Human-Computer Interaction
Wearable Technology

Background:

Surface electromyography (sEMG) is a promising technology for human-machine interfaces, particularly when visual methods are impractical.
sEMG-based gesture recognition faces challenges due to movement artifacts, individual muscle variations, and hand position changes, limiting dynamic gesture recognition.
Existing research predominantly focuses on static hand positions, hindering real-world applications.

Purpose of the Study:

To develop and evaluate an integrated system for finger gesture recognition using soft wearable sEMG sensors.
To improve the accuracy and robustness of sEMG-based gesture recognition across both static and dynamic hand positions.
To assess the potential of a Video-Vision-Transform model combined with motion sensor training for predicting finger joint angles and recognizing gestures.

Main Methods:

Integration of a soft wearable sEMG sensor with a Video-Vision-Transform (ViT) model.
Utilizing motion sensor-based training to enhance gesture recognition capabilities.
Testing the system's performance in differentiating finger angles and recognizing gestures in static and dynamic conditions.

Main Results:

The integrated system demonstrated the ability to differentiate finger angles and recognize gestures across subjects.
Recognition accuracy reached 0.85 for static and 0.87 for dynamic gestures in the best-performing subject.
The system showed stable performance across both static and dynamic conditions, despite inter-subject variability.

Conclusions:

This study advances sEMG-based finger gesture recognition by achieving stable performance in both static and dynamic scenarios.
The developed approach shows potential for natural and real-world human-machine interface applications.
Further research may address inter-subject variability to broaden applicability.