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[Online]Lightweight EMG Gesture Recognition Using Mixed-Precision Convolutional Neural Networks
Lightweight EMG Gesture Recognition Using Mixed-Precision Convolutional Neural Networks
ID：216
Submission ID：55 View Protection：ATTENDEE
Updated Time：2025-12-26 09:50:57 Hits：458
Online
Start Time：2025-12-30 17:00 （Asia/Amman）
Duration：15min
Session：[S7] Track 7: Pattern Recognition, Computer Vision and Image Processing » [S7-2] Track 7: Pattern Recognition, Computer Vision and Image Processing
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Abstract
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.
Keywords
EMG signal detection,convolutional neural network,mixed-precision quantization,gesture recognition,hardware-friendly
Speaker
Siyuan Shen
Student Southeast UniversitySubmission Author
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