High-sensitivity flexible triboelectric nanogenerator sensor based on recycled PA66 for the monitoring of soccer player lower limb training

IF 5.7 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Materials Research Bulletin Pub Date : 2025-09-08 DOI:10.1016/j.materresbull.2025.113774

Jiayi Zhang , Qianyue Li , Jiecong Li , Yun Zhang , Yunping Shen , Lian Zeng , Guangwu Sun , Changfa Xiao

{"title":"High-sensitivity flexible triboelectric nanogenerator sensor based on recycled PA66 for the monitoring of soccer player lower limb training","authors":"Jiayi Zhang , Qianyue Li , Jiecong Li , Yun Zhang , Yunping Shen , Lian Zeng , Guangwu Sun , Changfa Xiao","doi":"10.1016/j.materresbull.2025.113774","DOIUrl":null,"url":null,"abstract":"<div><div>Modern competitive sports demand advanced athlete motion monitoring, yet current wearables face sensitivity and material limitations. This study proposes a novel highly sensitive triboelectric nanogenerator sensor (PN-Sensor) based on recycled nylon 66. This multilayer flexible structure demonstrates the following characteristics: gradient sensitivity within a 0–20 N loading range, achieving 7.4837 V/N sensitivity in the low-load regime (0–10 N) with further enhancement to 32.3558 V/N in the medium-high load range (10–20 N). It exhibits a rapid 91 ms response time and maintains minimal signal deviation after 10,000 cyclic durability tests. Integrated at lower-limb biomechanical nodes with Bluetooth transmission and multimodal analysis, the system enables real-time monitoring of four football-specific movements. The posture recognition system based on a Convolutional Neural Network achieves 97.5 % accuracy in movement classification, significantly enhancing training analytical efficacy. This eco-friendly sensor provides innovative industrial waste upcycling while demonstrating significant potential for intelligent sports applications.</div></div>","PeriodicalId":18265,"journal":{"name":"Materials Research Bulletin","volume":"194 ","pages":"Article 113774"},"PeriodicalIF":5.7000,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Research Bulletin","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0025540825004817","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

Modern competitive sports demand advanced athlete motion monitoring, yet current wearables face sensitivity and material limitations. This study proposes a novel highly sensitive triboelectric nanogenerator sensor (PN-Sensor) based on recycled nylon 66. This multilayer flexible structure demonstrates the following characteristics: gradient sensitivity within a 0–20 N loading range, achieving 7.4837 V/N sensitivity in the low-load regime (0–10 N) with further enhancement to 32.3558 V/N in the medium-high load range (10–20 N). It exhibits a rapid 91 ms response time and maintains minimal signal deviation after 10,000 cyclic durability tests. Integrated at lower-limb biomechanical nodes with Bluetooth transmission and multimodal analysis, the system enables real-time monitoring of four football-specific movements. The posture recognition system based on a Convolutional Neural Network achieves 97.5 % accuracy in movement classification, significantly enhancing training analytical efficacy. This eco-friendly sensor provides innovative industrial waste upcycling while demonstrating significant potential for intelligent sports applications.

Abstract Image

查看原文本刊更多论文

基于再生PA66的高灵敏度柔性摩擦电纳米发电机传感器用于足球运动员下肢训练监测

现代竞技体育需要先进的运动员运动监测，但目前的可穿戴设备面临灵敏度和材料的限制。本研究提出了一种基于再生尼龙66的新型高灵敏度摩擦电纳米发电机传感器（PN-Sensor）。该多层柔性结构具有以下特点：0-20 N负载范围内的梯度灵敏度，低负载（0-10 N）灵敏度为7.4837 V/N，中高负载（10-20 N）灵敏度进一步提高到32.3558 V/N。它具有快速91毫秒的响应时间，并在10,000次循环耐久性测试后保持最小的信号偏差。该系统通过蓝牙传输和多模态分析将下肢生物力学节点集成在一起，可以实时监控四种足球特定动作。基于卷积神经网络的姿态识别系统的动作分类准确率达到97.5%，显著提高了训练分析效率。这种环保传感器提供了创新的工业废物升级回收，同时展示了智能体育应用的巨大潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Materials Research Bulletin 工程技术-材料科学：综合

CiteScore

9.80

自引率

5.60%

发文量

372

审稿时长

42 days

期刊介绍： Materials Research Bulletin is an international journal reporting high-impact research on processing-structure-property relationships in functional materials and nanomaterials with interesting electronic, magnetic, optical, thermal, mechanical or catalytic properties. Papers purely on thermodynamics or theoretical calculations (e.g., density functional theory) do not fall within the scope of the journal unless they also demonstrate a clear link to physical properties. Topics covered include functional materials (e.g., dielectrics, pyroelectrics, piezoelectrics, ferroelectrics, relaxors, thermoelectrics, etc.); electrochemistry and solid-state ionics (e.g., photovoltaics, batteries, sensors, and fuel cells); nanomaterials, graphene, and nanocomposites; luminescence and photocatalysis; crystal-structure and defect-structure analysis; novel electronics; non-crystalline solids; flexible electronics; protein-material interactions; and polymeric ion-exchange membranes.