Development of a Research-Oriented Application for the Acquisition, Analysis, and Export of Sleep Metrics from Smartwatches

Advances in wearable technology have significantly enhanced the ability to monitor sleep in naturalistic, real-world environments, providing valuable insights beyond traditional laboratory-based methods. Smartwatches, equipped with sensors such as accelerom-eters, gyroscopes, and photoplethysmography (PPG), offer an accessible means to collect continuous sleep-related data. However, the limited access to raw sensor data and the use of proprietary algorithms in most commercial devices present substantial challenges for researchers aiming for transparency, reproducibility, and methodological flexibility. In response to this gap, this work introduces the development of a research-oriented software application specifically designed to enable the efficient extraction, visualization, and exportation of sleep metrics from smartwatches. The system empowers researchers to configure data acquisition parameters, access both processed metrics and raw sensor readings, and export data in customizable formats, such as CSV and JSON, thereby facilitating downstream scientific analysis. Particular attention was given to creating a user-friendly interface optimized for mobile devices, along with secure data handling mechanisms. This work highlights the growing importance of customizable, open-access tools in sleep research, offering a flexible alternative to closed commercial ecosystems. By bridging the gap between consumer devices and academic research needs, the proposed solution paves the way for broader adoption of wearable technology in decentralized sleep studies and fosters new possibilities for personalized health monitoring and longitudinal sleep assessment in diverse populations.