在低成本硬件上实现低延迟肌电控制，兼顾精度与资源限制。
Pareto-Optimal Model Selection for Low-Cost, Single-Lead EMG Control in Embedded Systems
- 用18种模型对比，筛选出适合嵌入式设备的最优方案。
- 随机森林达74%准确率，满足<100ms延迟、<320KB内存约束。
- 自研网络虽精度99%，但仅适用于高性能边缘加速器。
消费级生物传感器将肌电（EMG）系统成本从数千美元降至约13美元，但引入显著信号不稳定和运动伪影。在资源受限的边缘设备（如ESP32）部署机器学习模型面临挑战：需在分类准确率、延迟（<100ms）和内存（<320KB）间权衡。基于单人数据集（1,540秒原始数据，154万点，分割为约1,300个1秒窗口），评估18种模型架构，涵盖统计启发式方法至深度迁移学习（ResNet50）及自研混合网络（MaxCRNN）。尽管自研“MaxCRNN”（Inception + Bi-LSTM + Attention）达到99%精确率，表现出最高安全性与鲁棒性，但随机森林（74%准确率）被识别为在老旧微控制器上的帕累托最优解。结果表明，可靠、低延迟的肌电控制可在通用硬件上实现，深度学习则为现代边缘AI加速器提供接近完美的可靠性路径。
原文摘要 · Abstract (English)
Consumer-grade biosensors offer a cost-effective alternative to medical-grade electromyography (EMG) systems, reducing hardware costs from thousands of dollars to approximately $13. However, these low-cost sensors introduce significant signal instability and motion artifacts. Deploying machine learning models on resource-constrained edge devices like the ESP32 presents a challenge: balancing classification accuracy with strict latency (<100ms) and memory (<320KB) constraints. Using a single-subject dataset comprising 1,540 seconds of raw data (1.54M data points, segmented into ~1,300 one-second windows), I evaluate 18 model architectures, ranging from statistical heuristics to deep transfer learning (ResNet50) and custom hybrid networks (MaxCRNN). While my custom "MaxCRNN" (Inception + Bi-LSTM + Attention) achieved the highest safety (99% Precision) and robustness, I identify Random Forest (74% accuracy) as the Pareto-optimal solution for embedded control on legacy microcontrollers. I demonstrate that reliable, low-latency EMG control is feasible on commodity hardware, with Deep Learning offering a path to near-perfect reliability on modern Edge AI accelerators.
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