Pareto-Optimal Model Selection for Low-Cost, Single-Lead EMG Control in Embedded Systems

• URL: http://arxiv.org/abs/2601.06516v1
• Date: Sat, 10 Jan 2026 10:33:47 GMT
• Title: Pareto-Optimal Model Selection for Low-Cost, Single-Lead EMG Control in Embedded Systems
• Abstract summary: Consumer-grade biosensors offer a cost-effective alternative to medical-grade electromyography (EMG) systems.<n>These low-cost sensors introduce significant signal instability and motion artifacts.<n>I demonstrate that reliable, low-latency EMG control is feasible on commodity hardware.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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