数字创新中心

Center for Digital Innovation

Designing Smart Legging for Posture Monitoring Based on Textile Sensing Networks

Qi Wang, Fang Cui, Runhua Zhang, Leheng Chen, Jialin Yuan, Xiaohua Sun, Bin Yu

UIST ’23

摘要/Abstract

跑步是一种极受欢迎的锻炼形式,但长期不正确的跑步姿势可能导致严重的膝关节损伤。近期,智能织物在持续运动监测方面展示了显著的潜力。本研究涉及一种智能打底裤的设计与开发,该打底裤配备了电阻性织物传感器网络,以监测下肢运动。研究主要分为三个部分。首先,我们测试了织物传感器的线性和鲁棒性,以确定能够监测跑步姿势特征的基本传感器单元。接着,通过对比实验确定了最佳传感器位置,并提出了传感器网络。最后,基于LSTM模型和来自6名参与者的数据,我们开发了能够以99.1%的准确率识别三种不当跑步姿势和正常姿势的智能打底裤系统。评估结果表明,该智能打底裤系统具有通过持续监测和多模态反馈帮助用户调整跑步姿势,以防止膝关节损伤的潜力。

Running is a highly popular form of exercise, while incorrect running posture over an extended period can lead to severe knee injuries. Smart textiles have recently demonstrated significant potential for continuous motion monitoring. This study involved the design and development of a smart legging with a resistive textile sensor network to monitor lower body motion. The study consists of three main parts. Firstly, we tested textile sensors in terms of linearity and robustness to determine the basic sensor unit that can monitor the characteristics of running postures. Next, optimal sensor placement was determined through comparison experiments, and a sensor network was proposed. Finally, based on the LSTM model with data gathered from 6 participants, we developed the smart legging system that is capable of identifying three types of improper running postures and normal postures with 99.1% accuracy. The evaluation revealed that the smart legging system had the potential to help users adjust their running postures to prevent knee injury through continuous monitoring and multi-modal feedback.

相关信息/Info

作者/Authors

链接/Link

Qi Wang, Fang Cui, Runhua Zhang, Leheng Chen, Jialin Yuan, Xiaohua Sun, Bin Yu

https://www.tandfonline.com/doi/full/10.1080/10447318.2023.2261704

图片/Figures