Edge AI on Constrained Devices for Binary Sleep-Wake Classification in Dynamic Environments

arXiv:2609.29163 · cs.LG · Submitted 2026-09-24 · Read on arXiv

cs.LG

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/Reitmania/esp32

Terminology

• Core — 20× in this paper · explained in 2 episodes
• Edge AI — 18× in this paper · explained in 2 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• Quantization — 6× in this paper · explained in 12 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Edge Devices — 2× in this paper · explained in 2 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• AUC — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Bluetooth Low Energy — 1× in this paper
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Verification — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
• TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems
• Knight Watch: Ubiquitous Computing Enhancements To Sleep Quality With Acoustic Analysis
• TensorFlow: A system for large-scale machine learning
• Adam: A Method for Stochastic Optimization

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