用量化技术让指尖超声手势识别在树莓派上实时运行
Forearm Ultrasound based Gesture Recognition on Edge
- 用量化压缩模型，让深度网络在边缘设备运行
- 树莓派上达92%准确率，推理仅需0.31秒
- 为可穿戴超声手势系统提供可行方案
前臂超声成像在手部手势分类方面展现出显著潜力。尽管如此，针对独立端到端手势识别系统的开发仍较少，导致其难以移动化、实时化和用户友好。为此，本文探索了在边缘设备上部署深度神经网络进行前臂超声手势识别的方法。通过使用量化技术，我们大幅减少了模型体积，同时保持高精度与低延迟。最佳模型采用Float16量化，在树莓派上实现92%的测试准确率和0.31秒的推理时间。这些结果证明了在资源受限的边缘设备上实现高效、实时手势识别的可行性，为可穿戴超声基系统铺平道路。
原文摘要 · Abstract (English)
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.
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