Pareto-Optimal Model Selection for Low-Cost, Single-Lead EMG Control in Embedded Systems
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 (
Fields of Study & Subjects
Muscle activation and electromyography studies
Context-Aware Activity Recognition Systems
Hand Gesture Recognition Systems
Publication details
- Journal / source
- arXiv (Cornell University)
- Publication date
- 10 January 2026
- Document type
- Preprint