报告详情
Lightweight EMG Gesture Recognition Using Mixed-Precision Convolutional Neural Networks
编号：216
访问权限：仅限参会人
更新：2025-12-26 09:50:57 浏览：75次
拓展类型2
摘要
Abstract—Convolutional neural networks (CNNs) are widely used in EMG-based motion recognition but typically incur high computational and memory costs, limiting deployment on edge or wearable platforms. To address this challenge, this paper proposes a lightweight CNN model based on mixed-precision quantization. By assigning different bit-widths (2/4/8 bits) to each network layer according to its sensitivity to precision, the proposed method significantly reduces both model size and inference complexity while maintaining classification performance. Specifically, we design a CNN model targeting five-class EMG gesture recognition based on the Ninapro DB1 dataset. The original model achieves an accuracy of 92.68% under FP32 precision, with a model size of 252.4 KB. By applying a layer-wise mixed-precision quantization strategy combining post-training quantization (PTQ) and quantization-aware training (QAT), the model is compressed to 36.58 KB (6.9× compression) with only a 3.1% accuracy drop. Hardware deployment on a RISC-V-based Milk-V Duo platform confirms the proposed approach’s suitability for embedded applications, achieving a 5.94× speedup over FP32 inference and a 1.65× improvement over INT8. These findings demonstrate the method’s potential for efficient, real-time EMG recognition in edge computing environments.
关键词
EMG signal detection,convolutional neural network,mixed-precision quantization,gesture recognition,hardware-friendly
报告人
Siyuan Shen
Student Southeast University稿件作者
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重要日期
-
会议日期
12月29日
2025
至12月31日
2025
-
12月30日 2025
报告提交截止日期
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12月30日 2025
注册截止日期
-
12月31日 2025
初稿截稿日期
主办单位
国际科学联合会
承办单位
扎尔卡大学
联系方式
- Miss AsianComNet
- as******@usssociety.org
历届会议
-
2024年10月24日 泰国 Bangkok
2024亚洲通讯与网络会议
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