Smart clothing has emerged as a promising Internet of Things (IoT)-based healthcare platform by enabling distributed physiological sensing through body sensor networks (BSNs). However, most systems rely on embedded chips and batteries, increasing complexity, weight, and cost that limit practicality. We present a battery-free chipless smart clothing that integrates thermoplastic polyurethane (TPU)-encapsulated passive LC relay networks to establish magnetic coupling between an external sensor reader and underlying arteries. Arterial pulsations induce resonant frequency shifts in the coupled system, enabling real-time pulse signal detection. Each sensing coil of passive LC relay networks is positioned at the carotid, heart, radial, and femoral arteries, and four pickup coils are routed to a centralized hub on the upper left arm. A lightweight wireless reader with four channels simultaneously acquires multisite physiological signals. Importantly, passive LC relay networks serve as magnetic field repeaters, enhancing magnetic coupling across extended distances. By matching resonant frequencies of passive LC relay networks and sensor reader at 7.3 MHz, the system achieved a power transfer efficiency (PTE) of 46.56%, clearly outperforming the capacitor-free configuration, which yielded only 6%. Notably, a minimum PTE threshold of 0.24% was established for distinguishable pulse detection. The system showed robustness after water-spray exposure and supported signal acquisition through outer garments up to 21.22 mm thick between the sensor reader and centralized hub, confirming resilience to sweat and compatibility with layered clothing. Overall, our battery-free chipless BSN offers lightweight, low-cost, and disposable smart clothing solutions for simultaneous multisite hemodynamic monitoring, potentially calculating pulse wave velocity for peripheral vascular stiffness assessment.
- Passive LC Relay Network-Enabled Battery-Free Chipless IoT Smart Clothing for Simultaneous Multisite Hemodynamic Monitoring
- Guan-Lin Su - National Tsing Hua UniversityEn-Zhu Lyu - National Tsing Hua UniversityTing-Wei Wang (Corresponding Author) - National Tsing Hua University
- IEEE
- 24
- National Science and Technology Council of Taiwan: 111-2222-E-007-005-MY3, 114-2221-E-007-039
This work was supported by the National Science and Technology Council of Taiwan under Grant 111-2222-E-007-005-MY3 and Grant 114-2221-E-007-039.
- Journal article
- 01/03/2026
- IEEE internet of things journal, Vol.13(5), pp.10076-10099
- English