# A Novel Multi-Modal Flexible Headband System for Sleep Monitoring

## TL;DR

This paper introduces a comfortable, portable headband system for sleep monitoring that can collect multiple physiological signals and accurately track sleep stages at home.

## Contribution

A novel flexible headband system with multi-modal sensing and compatibility with advanced sleep staging models for home-based sleep monitoring.

## Key findings

- The system achieved signal fidelity comparable to PSG with low power consumption and ultra-low input noise.
- Using the HBSleep dataset, the system achieved over 75% accuracy in sleep staging with machine learning models.
- AttnSleepNet was identified as the top-performing model for sleep staging with the headband system.

## Abstract

Sleep monitoring is critical for diagnosing and treating sleep disorders. Although polysomnography (PSG) remains the clinical gold standard, its complexity, discomfort, and lack of portability limit its applicability for long-term and home-based monitoring. To overcome these challenges, this study introduces a novel flexible headband system designed for multi-modal physiological signal acquisition, incorporating dry electrodes, a six-axis inertial measurement unit (IMU), and a temperature sensor. The device supports eight EEG channels and enables wireless data transmission via Bluetooth, ensuring user convenience and reliable long-term monitoring in home environments. To rigorously evaluate the system’s performance, we conducted comprehensive assessments involving 13 subjects over two consecutive nights, comparing its outputs with conventional PSG. Experimental results demonstrate the system’s low power consumption, ultra-low input noise, and robust signal fidelity, confirming its viability for overnight sleep tracking. Further validation was performed using the self-collected HBSleep dataset (over 184 h recordings of the 13 subjects), where state-of-the-art sleep staging models (DeepSleepNet, TinySleepNet, and AttnSleepNet) were applied. The system achieved an overall accuracy exceeding 75%, with AttnSleepNet emerging as the top-performing model, highlighting its compatibility with advanced machine learning frameworks. These results underscore the system’s potential as a reliable, comfortable, and practical solution for accurate sleep monitoring in non-clinical settings.