  The Internet of Medical Things (IoMT) is a platform that combines Internet of
Things (IoT) technology with medical applications, enabling the realization of
precision medicine, intelligent healthcare, and telemedicine in the era of
digitalization and intelligence. However, the IoMT faces various challenges,
including sustainable power supply, human adaptability of sensors and the
intelligence of sensors. In this study, we designed a robust and intelligent
IoMT system through the synergistic integration of flexible wearable
triboelectric sensors and deep learning-assisted data analytics. We embedded
four triboelectric sensors into a wristband to detect and analyze limb
movements in patients suffering from Parkinson's Disease (PD). By further
integrating deep learning-assisted data analytics, we actualized an intelligent
healthcare monitoring system for the surveillance and interaction of PD
patients, which includes location/trajectory tracking, heart monitoring and
identity recognition. This innovative approach enabled us to accurately capture
and scrutinize the subtle movements and fine motor of PD patients, thus
providing insightful feedback and comprehensive assessment of the patients
conditions. This monitoring system is cost-effective, easily fabricated, highly
sensitive, and intelligent, consequently underscores the immense potential of
human body sensing technology in a Health 4.0 society.
