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Robust hydrogel sensors for unsupervised learning enabled sign-to-verbal translation 被引量:2
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作者 Hude Ma Haiyang Qin +12 位作者 Xiao Xiao Na Liu Shaolei Wang Junye Li Sophia Shen Shuqi Dai Mengmeng Sun Peiyi Li Xiaofang Pan Mingjun Huang Baoyang Lu Jun Chen Lidong Wu 《InfoMat》 SCIE CSCD 2023年第7期70-80,共11页
Highly stretchable and robust strain sensors are rapidly emerging as promising candidates for a diverse of wearable electronics.The main challenge for the practical application of wearable electronics is the energy co... Highly stretchable and robust strain sensors are rapidly emerging as promising candidates for a diverse of wearable electronics.The main challenge for the practical application of wearable electronics is the energy consumption and device aging.Energy consumption mainly depends on the conductivity of the sensor,and it is a key factor in determining device aging.Here,we design a liq-uid metal(LM)-embedded hydrogel as a sensing material to overcome the bar-rier of energy consumption and device aging of wearable electronics.The sensing material simultaneously exhibits high conductivity(up to 22 S m�1),low elastic modulus(23 kPa),and ultrahigh stretchability(1500%)with excel-lent robustness(consistent performance against 12000 mechanical cycling).A motion monitoring system is composed of intrinsically soft LM-embedded hydrogel as sensing material,a microcontroller,signal-processing circuits,Bluetooth transceiver,and self-organizing map developed software for the visu-alization of multi-dimensional data.This system integrating multiple functions including signal conditioning,processing,and wireless transmission achieves monitor hand gesture as well as sign-to-verbal translation.This approach provides an ideal strategy for deaf-mute communicating with normal people and broadens the application of wearable electronics. 展开更多
关键词 HYDROGEL liquid metal sign-to-verbal translation strain sensor unsupervised learning
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