Yang Huang¹ and Andrew Park², ¹USA, ²California State Polytechnic University, USA

Sleep is essential for human health, yet millions suffer from insufficient or poor-quality rest. Traditional solutions such as polysomnography are accurate but impractical for continuous home use, while commercial devices often provide limited insights [1]. This paper introduces SleepEase, a mobile application and sensor-equipped hardware system that monitors sleep and delivers adaptive soundscapes to support faster sleep onset. Three core components-mobile app, hardware device, and Firebase backend-work together to provide monitoring, real-time feedback, and long-term data storage [2]. Challenges such as sleep detection accuracy, hardware design, and sound personalization were addressed through careful integration of multiple sensors and adaptive audio options. Experiments demonstrated that white noise and ocean sound reduced sleep latency, while enhanced detection algorithms achieved higher precision and recall compared to baseline methods. Compared with prior methodologies, SleepEase improves accuracy and personalization by combining monitoring with intervention. Ultimately, it presents a practical, scalable solution for at-home sleep improvement.

Sleep onset latency, Adaptive audio, White noise, Home health technology, Mobile health