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  5. Triboelectric Nanogenerator Enabled Body Sensor Network for Self-Powered Human Heart-Rate Monitoring

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Article
en
2017

Triboelectric Nanogenerator Enabled Body Sensor Network for Self-Powered Human Heart-Rate Monitoring

0 Datasets

0 Files

en
2017
Vol 11 (9)
Vol. 11
DOI: 10.1021/acsnano.7b02975

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Zhiming Lin
Jun Chen
Xiaoshi Li
+5 more

Abstract

Heart-rate monitoring plays a critical role in personal healthcare management. A low-cost, noninvasive, and user-friendly heart-rate monitoring system is highly desirable. Here, a self-powered wireless body sensor network (BSN) system is developed for heart-rate monitoring via integration of a downy-structure-based triboelectric nanogenerator (D-TENG), a power management circuit, a heart-rate sensor, a signal processing unit, and Bluetooth module for wireless data transmission. By converting the inertia energy of human walking into electric power, a maximum power of 2.28 mW with total conversion efficiency of 57.9% was delivered at low operation frequency, which is capable of immediately and sustainably driving the highly integrated BSN system. The acquired heart-rate signal by the sensor would be processed in the signal process circuit, sent to an external device via the Bluetooth module, and displayed on a personal cell phone in a real-time manner. Moreover, by combining a TENG-based generator and a TENG-based sensor, an all-TENG-based wireless BSN system was developed, realizing continuous and self-powered heart-rate monitoring. This work presents a potential method for personal heart-rate monitoring, featured as being self-powered, cost-effective, noninvasive, and user-friendly.

How to cite this publication

Zhiming Lin, Jun Chen, Xiaoshi Li, Zhihao Zhou, Keyu Meng, Wei Wei, Jin Yang, Zhong Lin Wang (2017). Triboelectric Nanogenerator Enabled Body Sensor Network for Self-Powered Human Heart-Rate Monitoring. , 11(9), DOI: https://doi.org/10.1021/acsnano.7b02975.

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Publication Details

Type

Article

Year

2017

Authors

8

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1021/acsnano.7b02975

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