At-home wireless sleep monitoring patches for the clinical assessment of sleep quality and sleep apnea

Shinjae Kwon, Hyeon Seok Kim, Kangkyu Kwon, Hodam Kim, Yun Soung Kim, Sung Hoon Lee, Young Tae Kwon, Jae Woong Jeong, Lynn Marie Trotti, Audrey Duarte, Woon Hong Yeo

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Although many people suffer from sleep disorders, most are undiagnosed, leading to impairments in health. The existing polysomnography method is not easily accessible; it's costly, burdensome to patients, and requires specialized facilities and personnel. Here, we report an at-home portable system that includes wireless sleep sensors and wearable electronics with embedded machine learning. We also show its application for assessing sleep quality and detecting sleep apnea with multiple patients. Unlike the conventional system using numerous bulky sensors, the soft, all-integrated wearable platform offers natural sleep wherever the user prefers. In a clinical study, the face-mounted patches that detect brain, eye, and muscle signals show comparable performance with polysomnography. When comparing healthy controls to sleep apnea patients, the wearable system can detect obstructive sleep apnea with an accuracy of 88.5%. Furthermore, deep learning offers automated sleep scoring, demonstrating portability, and point-of-care usability. At-home wearable electronics could ensure a promising future supporting portable sleep monitoring and home healthcare.

Original languageEnglish
Article numbereadg9671
JournalScience advances
Issue number21
StatePublished - May 2023


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