# Design and Implementation of an IoT-Based Low-Power Wearable EEG Sensing System for Home-Based Sleep Monitoring

## TL;DR

This paper introduces a low-power wearable EEG system for home sleep monitoring that balances accuracy and energy efficiency.

## Contribution

The novel contribution is a co-designed hardware-software system with a lightweight deep learning model for energy-efficient sleep staging.

## Key findings

- The system achieves 24.6 hours of continuous operation on a 1000 mAh battery with 150.85 mW average power consumption.
- The deployed SleePyCo model achieved 79.3% overall accuracy and 88.3% F1-score for Deep Sleep (N3) on the ISRUC dataset.
- Field trials showed a valid data rate over 97% and a Bluetooth packet loss rate of 0.8%.

## Abstract

Long-term home-based sleep monitoring requires wearable sensing devices that strictly balance signal precision with power constraints. This study presents the design and implementation of a low-noise, low-power wearable single-channel electroencephalography (EEG) system for automatic sleep staging. The hardware architecture integrates a TI ADS1298 analog front-end with an STM32F4 microcontroller, utilizing differential sampling and hardware-based filtering to effectively suppress power-line interference and baseline drift. System-level testing demonstrates an average power consumption of approximately 150.85 mW, enabling over 24.6 h of continuous operation on a 1000 mAh battery, which meets the requirements for overnight monitoring. To achieve accurate staging without draining the wearable’s battery, we adopted and deployed a lightweight deep learning model, SleePyCo, on the cloud backend. This architecture was specifically optimized for our edge–cloud collaborative execution, which combines contrastive representation learning with temporal dependency modeling. Validation on the ISRUC dataset yielded an overall accuracy of 79.3% ± 3.0%, with a notable F1-score of 88.3% for Deep Sleep (N3). Furthermore, practical field trials involving 10 healthy subjects verified the system’s engineering stability, achieving a valid data rate exceeding 97% and a Bluetooth packet loss rate of only 0.8%. These results confirm that the proposed hardware–software co-designed system provides a robust, energy-efficient IoMT sensing solution for daily sleep health management.