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The Internet of Medical Things (IoMT) is a platform that combines Internet ofThings (IoT) technology with medical applications, enabling the realization ofprecision medicine, intelligent healthcare, and telemedicine in the era ofdigitalization and intelligence. However, the IoMT faces various challenges,including sustainable power supply, human adaptability of sensors and theintelligence of sensors. In this study, we designed a robust and intelligentIoMT system through the synergistic integration of flexible wearabletriboelectric sensors and deep learning-assisted data analytics. We embeddedfour triboelectric sensors into a wristband to detect and analyze limbmovements in patients suffering from Parkinson's Disease (PD). By furtherintegrating deep learning-assisted data analytics, we actualized an intelligenthealthcare monitoring system for the surveillance and interaction of PDpatients, which includes location/trajectory tracking, heart monitoring andidentity recognition. This innovative approach enabled us to accurately captureand scrutinize the subtle movements and fine motor of PD patients, thusproviding insightful feedback and comprehensive assessment of the patientsconditions. This monitoring system is cost-effective, easily fabricated, highlysensitive, and intelligent, consequently underscores the immense potential ofhuman body sensing technology in a Health 4.0 society.

A Health Monitoring System Based on Flexible Triboelectric Sensors for  Intelligence Medical Internet of Things and its Applications in Virtual  Reality