Deep Learning Assisted Body Area Triboelectric Hydrogel Sensor Network for Infant Care. (15th June 2022)

• Record Type: Journal Article
• Authors: Guo, Rui; Fang, Yunsheng; Wang, Zhaosu; Libanori, Alberto; Xiao, Xiao; Wan, Dong; Cui, Xiaojing; Sang, Shengbo; Zhang, Wendong; Zhang, Hulin; Chen, Jun
• Is Part Of: Advanced functional materials. Volume 32:Number 35(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-06-15
• Subjects: body area sensor networks -- deep learning -- hydrogels -- infant care -- triboelectric sensors; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adfm.202204803
• Languages: English
• ISSNs: 1616-301X

Abstract

Abstract: Infants are physically vulnerable and cannot express their feelings. Continuous monitoring and measuring the biomechanical pressure to which an infant body is exposed remains critical to avoid infant injury and illness. Here, a body area sensor network comprising edible triboelectric hydrogel sensors for all‐around infant motion monitoring is reported. Each soft sensor holds a collection of compelling features of high signal‐to‐noise ratio of 23.1 dB, high sensitivity of 0.28 V kPa −1, and fast response time of 50 ms. With the assistance of deep learning algorithms, the body area sensor network can realize infant motion pattern identification and recognition with classification accuracy as high as 100%. Additionally, a customized user‐friendly cellphone application is developed to provide real‐time warning and one‐click guardian interaction. This self‐powered body area sensor network system provides a promising paradigm for reliable infant care in the era of the Internet of Things. Abstract : A body area sensor network based on edible triboelectric hydrogel sensors is constructed, which can realize the measurement of mechanical pressure and recognition of multiple motion patterns of infants with the assistance of deep learning algorithms. Moreover, a customized user‐friendly cellphone application is developed to provide a promising paradigm for infant care with real‐time warning and one‐click guardian interaction mode.