Qian Chen, Duo Xu, Yan Yan, Zhan Qu, Haoyue Zhao, Xinyu Li, Yuying Cao, Chenhong Lang, Wasim Akram, Zhe Sun, Li Niu, Jian Fang
{"title":"A Self‐powered Tennis Training System Based on Micro‐Nano Structured Sensing Yarn Arrays","authors":"Qian Chen, Duo Xu, Yan Yan, Zhan Qu, Haoyue Zhao, Xinyu Li, Yuying Cao, Chenhong Lang, Wasim Akram, Zhe Sun, Li Niu, Jian Fang","doi":"10.1002/adfm.202414395","DOIUrl":null,"url":null,"abstract":"Wearable sensing devices can reliably track players' mobility, revolutionizing sports training. However, current sensing electronics face challenges due to their complex structures, battery dependence, and unreliable sensing signals. Here, a tennis training system is demonstrated using machine learning based on elastic self‐powered sensing yarns. By employing a simple and effective strategy, piezoelectric nanofibers and triboelectric materials are integrated into a single yarn, enabling the simultaneous translation of both triboelectric and piezoelectric signals. Additionally, these yarns exhibit outstanding processability, allowing them to be machine‐knitted into self‐powered sensing fabrics. Due to their great sensitivity, these sensing yarns and fabrics may detect human movement with great precision. Machine learning algorithms can classify and interpret these signals to recognize various human motions. The developed tennis training system aims to maximize its benefits and provide comprehensive training for both players and coaches. This work enhances the applicability of self‐powered sensing systems in smart sports monitoring and training, advancing the field of intelligent sports training.","PeriodicalId":112,"journal":{"name":"Advanced Functional Materials","volume":null,"pages":null},"PeriodicalIF":18.5000,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Functional Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1002/adfm.202414395","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Wearable sensing devices can reliably track players' mobility, revolutionizing sports training. However, current sensing electronics face challenges due to their complex structures, battery dependence, and unreliable sensing signals. Here, a tennis training system is demonstrated using machine learning based on elastic self‐powered sensing yarns. By employing a simple and effective strategy, piezoelectric nanofibers and triboelectric materials are integrated into a single yarn, enabling the simultaneous translation of both triboelectric and piezoelectric signals. Additionally, these yarns exhibit outstanding processability, allowing them to be machine‐knitted into self‐powered sensing fabrics. Due to their great sensitivity, these sensing yarns and fabrics may detect human movement with great precision. Machine learning algorithms can classify and interpret these signals to recognize various human motions. The developed tennis training system aims to maximize its benefits and provide comprehensive training for both players and coaches. This work enhances the applicability of self‐powered sensing systems in smart sports monitoring and training, advancing the field of intelligent sports training.
期刊介绍:
Firmly established as a top-tier materials science journal, Advanced Functional Materials reports breakthrough research in all aspects of materials science, including nanotechnology, chemistry, physics, and biology every week.
Advanced Functional Materials is known for its rapid and fair peer review, quality content, and high impact, making it the first choice of the international materials science community.