Sleep2Optimize

The Sleep2Optimize project requires a meticulously designed experimental study to evaluate Nukkuaa's sleep classification technology, as implemented in the Sleep2 phone application, against leading competitors such as Apple Watch, Fitbit, and others. This study will involve collecting sleep data from a minimum of five participants over five consecutive nights, utilizing both commercial wearables and the gold standard, polysomnography—a method combining electroencephalography, electromyography, and electrooculography. The study is structured to capture day-to-day variability and assess classification performance under irregular sleep conditions, providing a robust analysis of Sleep2's capabilities and informing the development of novel machine-learning enhancements for improved sleep classification. The anticipated outcomes include detailed insights into Sleep2’s sleep classification accuracy, particularly in detecting wakefulness after sleep onset. Furthermore, a comprehensive comparison of Sleep2’s performance against competitors will be conducted using a standardized framework, ensuring the evaluation adheres to the highest scientific standards. The project also seeks to identify areas for improvement in Sleep2’s machine-learning algorithms, grounded in real-world data. The results will establish a clear benchmark for Sleep2’s technology, guiding future developments and ensuring the sleep classification system continues to meet and exceed industry expectations