A skin-interfaced wireless wearable device and data analytics approach for sleep-stage and disorder detection.

Accurate identification of sleep stages and disorders is crucial for maintaining health, preventing chronic conditions, and improving diagnosis and treatment. Direct respiratory measurements, as key biomarkers, are missing in traditional wrist- or finger-worn wearables, which thus limit their precision in detection of sleep stages and sleep disorders. By contrast, this work introduces a simple, multimodal, skin-integrated, energy-efficient mechanoacoustic sensor capable of synchronized cardiac and respiratory measurements.