Sleep Quality Analysis Platform
Data analytics platform that combines sensor data and self-reports to analyze sleep quality patterns.
Sleep Quality Analysis Platform
Research Goals
Investigate relationships between lifestyle factors, environmental conditions, and sleep quality through comprehensive data analysis.
Data Sources
- Wearable device data (heart rate, movement, temperature)
- Environmental sensors (light, noise, humidity)
- Daily self-report questionnaires (mood, stress, caffeine intake)
- Sleep diary entries with subjective quality ratings
Analytics Approach
Statistical modeling using R with machine learning algorithms in Python to identify predictive factors for sleep quality.
Key Findings
Identified significant correlations between evening light exposure and sleep onset latency, informing personalized sleep hygiene recommendations.