Forearm Ultrasound Based Gesture Recognition on Edge

Ultrasound imaging of the forearm has demon-strated significant potential for accurate hand gesture classification. Despite this progress, there has been limited focus on developing a stand -alone end-to-end gesture recognition system which makes it mobile, real-time and more user friendly. To bridg...

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Bibliographic Details
Main Authors: Bimbraw, Keshav, Zhang, Haichong K., Islam, Bashima
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Biomedical monitoring
Deep learning
Gesture recognition
Imaging
Low latency communication
Monitoring
On-Body Sensors
Quantization (signal)
Real-time systems
Signal Processing
Ultrasonic imaging
Ultrasound
Wearable Systems
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Summary:Ultrasound imaging of the forearm has demon-strated significant potential for accurate hand gesture classification. Despite this progress, there has been limited focus on developing a stand -alone end-to-end gesture recognition system which makes it mobile, real-time and more user friendly. To bridge this gap, this paper explores the deployment of deep neural networks for forearm ultrasound-based hand gesture recognition on edge devices. Utilizing quantization techniques, we achieve substantial reductions in model size while maintaining high accuracy and low latency. Our best model, with Float16 quantization, achieves a test accuracy of 92% and an inference time of 0.31 seconds on a Raspberry Pi. These results demonstrate the feasibility of efficient, real-time gesture recognition on resource-limited edge devices, paving the way for wearable ultrasound-based systems.
ISSN:2376-8894
DOI:10.1109/BSN63547.2024.10780669