Yan Shi, Heran Li, Liman Yang, Yixuan Wang, Zhibo Sun, Chi Zhang, Xianpeng Fu, Yanxia Niu, Chengwei Han, Fei Xie
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引用次数: 0
Abstract
Flexible wearable electronic devices play a vital role in daily monitoring, medical diagnosis, and human-computer interaction, and such devices have a great demand for portability, integration, comfort, and self-power. In this study, a triboelectric nanogenerator integrated into a flexible chest belt is proposed as a displacement sensor to monitor the displacement and frequency of thoracic expansion. Based on three parallel interpolation electrode structures with phase differences, the Triboelectric Nanogenerators's(TENG) output signal pulse number can characterize the sliding displacement, with a resolution of more than 1 mm and a durability of more than 700,000 cycles. Based on the flexible printed circuit processing technology, the volume of the sensor is less than 8.5 cm3, and the weight is less than 3.2 g, which improves the portability of the device. Based on wireless radio frequency technology, the collected signals are transmitted to the upper computer, and then the monitoring of respiratory physiological signals and the human-machine synchronous control of the ventilator are achieved within the overshoot of 1.5% and the control error of 5% through a simulation machine. This work provides a sensing method for daily and medical respiratory monitoring and demonstrates the enormous potential of frictional electric sensors in intelligent medical applications.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.