The quality and quantity of sleep are key drivers of overall health and wellbeing. Sleep monitoring can be used to diagnose certain diseases, help individuals plan their sleep, and use insights from their sleeping data to impose lifestyle changes for better health. Home-based sleep monitoring has been possible with the introduction of wearable technology and smart devices to the consumer market in recent years. However, these existing sleep tracking systems are often costly, invasive, and require users to adhere to certain usage protocols to gather data accurately. This paper presents Sleeptracks - a low-cost and non-obtrusive sleep monitoring system that makes use of a sensor-embedded pillow. The proposed system leverages on mobile and cloud technology to generate personalized sleep analytics – sleep duration, pattern, efficiency, and deficit. The prototype has established the architecture that could be use for further research and development.
For more details, you can read the full paper here.
Contribution & Tech Stack
This was my final project for the KSE 624 (Mobile and Pervasive Computing) course. The hardware component was prototyped using Bluno. On a high level view, the bluno sends sensor data to an android application which preprocesses it and in turn puts information on the cloud using Firebase Real-time Database. A web dashboard built in HTML5 retrieves the information from Firebase and displays it for user consumption.