Affiliation:
1. CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro‐Nano Energy and Sensor Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Science Beijing the People's Republic of China
2. Dalian Key Lab of Marine Micro/Nano Energy and Self‐Powered Systems Marine Engineering College, Dalian Maritime University Dalian the People's Republic of China
3. School of Nanoscience and Technology, University of Chinese Academy of Sciences Beijing the People's Republic of China
Abstract
AbstractThe inherent unpredictability of the maritime environment leads to low rates of survival during accidents. Life jackets serve as a crucial safety measure in underwater environments. Nonetheless, most conventional life jackets lack the capability to monitor the wearer's underwater body movements, impeding their effectiveness in rescue operations. Here, we present an intelligent self‐powered life jacket system (SPLJ) composed of a wireless body area sensing network, a set of deep learning analytics, and a human condition detection platform. Six coaxial core‐shell structure triboelectric fiber sensors with high sensitivity, stretchability, and flexibility are integrated into this system. Additionally, a portable integrated circuit module is incorporated into the SPLJ to facilitate real‐time monitoring of the wearer's movement. Moreover, by leveraging the deep‐learning‐assisted data analytics and establishing a robust correlation between the wearer's movements and condition, we have developed a comprehensive system for monitoring drowning individuals, achieving an outstanding recognition accuracy of 100%. This groundbreaking work introduces a fresh approach to underwater intelligent survival devices, offering promising prospects for advancing underwater smart wearable devices in rescue operations and the development of ocean industry.image
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献