Facile monitoring for human motion on fireground by using MiEs-TENG sensor

Abstract

Recently, wearable electronics is rapidly developed in personal protective technology. Human motion monitoring using wearable electronics to protect workers on fireground remains challenging, such as high fabrication cost, materials flammability, operation energy consumption, and insufficient sensor information. Herein, we report a facile method for constructing self-powered sensors based on the triboelectric nanogenerator (TENG) to monitor human motion on fireground. Through directly writing the patterned MXenes ink electrodes (MiEs) on the triboelectric materials, the prepared bend sensor and pressure sensor can perceive safety-related information based on human motion, such as simple emergency hand signs and complex gaits. Furthermore, the triboelectric output data collected from pressure sensor is trained by the support vector machine (SVM) algorithm to classify different gaits with an accuracy of 92.18%. Looking forward, these TENG sensors assembled by this method could be incorporated into worker’s clothes to monitor motion for personal protection application on fireground.