# Forearm Ultrasound based Gesture Recognition on Edge

arXiv: https://arxiv.org/abs/2409.09915 · HTML: https://mlsanity.com/paper/2409.09915/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 16 Sep 2024.

## Builds on (most cited first, 2 of 2)

- [Simultaneous Estimation of Hand Configurations and Finger Joint Angles using Forearm Ultrasound](https://mlsanity.com/paper/2211.15871/) (2022)
- [Prediction of Metacarpophalangeal joint angles and Classification of Hand configurations based on Ultrasound Imaging of the Forearm](https://mlsanity.com/paper/2109.11093/) (2021)

## Built on by (most cited first, 3 of 3)

- [Improving Intersession Reproducibility for Forearm Ultrasound based Hand Gesture Classification through an Incremental Learning Approach](https://mlsanity.com/paper/2409.16415/) (2024)
- [Hand Gesture Classification Based on Forearm Ultrasound Video Snippets Using 3D Convolutional Neural Networks](https://mlsanity.com/paper/2409.16431/) (2024)
- [Duo Streamers: A Streaming Gesture Recognition Framework](https://mlsanity.com/paper/2502.12297/) (2025)

## Abstract (arXiv)

Ultrasound imaging of the forearm has demonstrated 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.