Android Sleep Quality Monitoring System

BEng thesis, 80% accurate sleep quality monitoring using phone sensors with volunteer validation

Overview

Android-based sleep quality monitoring system using built-in smartphone sensors (accelerometer, light sensor, microphone) without external devices. Achieves 80% agreement with commercial wearables across 20-volunteer validation, with demonstrable sleep quality improvement over 2-week monitoring.

Problem Statement

Poor sleep quality affects cognitive function, increases chronic disease risk (diabetes, hypertension, depression), yet assessment relies on subjective questionnaires. Lab-based PSG is expensive and inaccessible; wearables have limited data access and proprietary formats. Need for objective, accessible, privacy-preserving daily monitoring.

Methodology

Sensor-Based Monitoring

  • Accelerometer: Body movement tracking for sleep vs. awake detection, posture analysis
  • Light Sensor: Environment adequacy assessment (target <10 lux), disruption monitoring
  • Microphone: Noise level detection (target <32 dB), snoring identification, respiratory pattern analysis

Sleep Quality Metrics

  • Sleep duration, efficiency [(sleep time / time in bed) × 100%]
  • Deep sleep ratio, breathing quality
  • Environment quality (light + noise)
  • Sleep stages: Awake, Light Sleep, Deep Sleep, Disruptions

Implementation

  • Stack: Kotlin, Android 8.0+ (API 26+), multi-threaded background service
  • Privacy: All processing on-device, no cloud dependency

Results

Validation (20 volunteers, 1-week monitoring):

  • 80% agreement with commercial wearables
  • Sleep onset/offset timing: ±15-20 minutes difference (button press vs. sensor detection)
  • Battery drain: 4-5% per 8-hour session, <3% CPU, <50 MB memory

Sleep Quality Improvement:

  • Week 1: Poor quality despite adequate duration (stress-related irregular sleep, poor breathing)
  • Week 2 (after behavioral recommendations): Improved deep sleep balance and breathing quality

Applications

  • Personal sleep quality tracking and optimization
  • Sleep disorder screening (sleep apnea, insomnia)
  • Circadian rhythm monitoring
  • Mental health assessment (sleep-mood correlation)
  • Academic/workplace performance optimization

Limitations & Future Work

Limitations: Button-press manual markers (±15-20min error), single-site validation, university student cohort only

Future Directions:

  • Automatic sleep onset/offset detection
  • Multi-demographic validation
  • Integration with wearable heart rate sensors

Achievements & Recognition

Key Metrics

  • 80% accuracy vs. commercial wearables
  • 20-volunteer validation with measurable QoL improvement
  • <5% battery drain per night, on-device privacy

Technical Stack

Kotlin, Android (multi-threading, background services), sensor fusion algorithms

Team & Collaboration

Supervisor: Asst Prof. Yu Zulong (余祖龙)
Institution: Department of Biomedical Engineering, Nanchang Hangkong University
Author: Zhou Yanuo (BEng graduation thesis)

Timeline

Duration: September 2022 - June 2023 (9 months, BEng thesis project)