Litcius/Paper detail

34.5 Human-Body-Coupled Power-Delivery and Ambient-Energy-Harvesting ICs for a Full-Body-Area Power Sustainability

Jiamin Li, Yilong Dong, Jeong Hoan Park, Longyang Lin, Tao Tang, Miaolin Zhang, Han Wu, Lian Zhang, Joanne Si Ying Tan, Jerald Yoo

202030 citationsDOI

Abstract

As the number of body area electronics increases, the power source is becoming a major bottleneck. Using a battery for each node is bulky and inconvenient due to the need of frequent battery replacement/recharging. Conventional harvesting methods are largely placement confined and thus unable to sustain multiple nodes of heterogeneous placements simultaneously [1], [2]. For simultaneous power delivery to multiple nodes on body, [3] utilizes the near-field inductive link for power delivery to sensor nodes placed on the eTextiles shirt and thus exhibits placement constraints, whereas the far-field RF transfer based [4] suffers from antenna pattern distortion and body shadowing effect (RF energy blocked by human body), leaving sensor nodes under such effect practically uncovered due to the 20~40 dB more channel degradation [5]. Such placement-induced nonidealities, along with distance/environment induced impedance variations and voltage/power degradations, challenges the powering system design for wider body coverage. This paper presents the full-body coverage “body-coupled power delivery” and the placement-independent “body-coupled ambient energy harvesting” ICs, achieved by the Bulk Adaptation Rectifier (BAR) and the Detuned Impedance Booster (DIB), with the power sustainability supported by the Dual Mode Buck-Boost Converter (DM-BBC).

Topics & Concepts

Maximum power transfer theoremElectrical engineeringEnergy harvestingWireless power transferPower managementPower (physics)Rectifier (neural networks)Wireless sensor networkElectrical impedanceComputer scienceEngineeringElectronic engineeringPhysicsElectromagnetic coilComputer networkRecurrent neural networkStochastic neural networkQuantum mechanicsArtificial neural networkMachine learningEnergy Harvesting in Wireless NetworksWireless Body Area NetworksWireless Power Transfer Systems