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.

翻译：消费级生物传感器为医疗级肌电图（EMG）系统提供了一种经济高效的替代方案，将硬件成本从数千美元降低至约13美元。然而，这些低成本传感器引入了显著的信号不稳定性和运动伪影。在ESP32等资源受限的边缘设备上部署机器学习模型面临一个挑战：在严格的延迟（<100ms）和内存（<320KB）约束下平衡分类准确性。使用包含1,540秒原始数据（154万个数据点，分割为约1,300个一秒窗口）的单被试数据集，我评估了18种模型架构，范围从统计启发式方法到深度迁移学习（ResNet50）以及自定义混合网络（MaxCRNN）。尽管我自定义的“MaxCRNN”（Inception + Bi-LSTM + Attention）实现了最高的安全性（99%精确率）和鲁棒性，但我确定随机森林（74%准确率）是传统微控制器上嵌入式控制的帕累托最优解。我证明了在商用硬件上实现可靠、低延迟的肌电控制是可行的，而深度学习为在现代边缘AI加速器上实现近乎完美的可靠性提供了一条路径。