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.

翻译：医疗物联网（IoMT）是一个将物联网技术与医疗应用相结合的平台，能够在数字化与智能化时代实现精准医疗、智慧医疗和远程医疗。然而，IoMT面临着可持续电源供应、传感器人体适应性及传感器智能化等多重挑战。本研究通过柔性可穿戴摩擦电传感器与深度学习辅助数据分析的协同集成，设计了一套稳健且智能的IoMT系统。我们将四个摩擦电传感器嵌入腕带中，用于检测和分析帕金森病（PD）患者的肢体运动。通过进一步集成深度学习辅助数据分析，我们实现了一套针对PD患者的智能健康监测系统，具备位置/轨迹追踪、心脏监测及身份识别功能。该创新方法使我们能够精准捕捉并剖析PD患者的细微动作与精细运动，从而为患者状况提供有洞察力的反馈和全面评估。该监测系统成本低廉、易于制造、灵敏度高且智能化，充分彰显了人体传感技术在健康4.0社会中的巨大潜力。