UC Irvine

In the medical field, there is a growing interest in continuous health monitoring as it has the potential toenhance the diagnosis and treatment of patients. However, to be effective, continuous monitoring systems need to be comfortable for patients to wear and able to gather reliable and actionable data. This often requires the device to be soft and conformal to ensure patient comfort and to provide a stable interface for high-quality signal acquisition. While wearable technology and global connectivity have experienced significant growth over the last few decades, progress in stretchable wearable electronics has been slower. This study focuses on developing a systematic and scalable approach to fabricate stretchable wearable systems for monitoring human vital signs. The research employs laser ablation to create stretchable substrates and integrates conventional surface mounted devices (SMD) with stretchable substrates. Lastly, a machine learning method is introduced to analyze vital signs collected from patients during sleep studies. The prediction of sleep stages and events is highly accurate, and there is great potential for conducting at-home sleep studies.