Designing Smart Legging for Posture Monitoring Based on Textile Sensing Networks

AbstractRunning 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.Keywords: Wearable technologytextile sensorposture monitoringmachine learning Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Flexpoint TM, https://flexpoint.com/2 Spectra Symbol, https://www.jameco.com/Jameco/Products/ProdDS/150551.pdf.Additional informationFundingThis project was funded by the National Natural Science Foundation of China (62202335) and the Science and Technology Commission of Shanghai Municipality (20YF1451200).Notes on contributorsQi WangQi Wang received her PhD degree from the Eindhoven University of Technology (Tu/e). She is currently an associate professor in the College of Design and Innovation, Tongji University. Her main research focused on wearable systems based on smart textiles for health.Fang CuiFang Cui received her Master of Engineering degree from the College of Design and Innovation, Tongji University, in 2021. Her main research focused on wearable systems based on smart textiles for motion monitoring.Runhua ZhangRunhua Zhang received her Bachelor of Engineering degree from Sichuan University, Sichuan, China, in 2021. She is currently a graduate student at the College of Design and Innovation, Tongji University, Shanghai, China. Her research interest focuses on Human-Computer Interaction.Leheng ChenLeheng Chen received his Bachelor of Engineering degree from Tongji University, Shanghai, China, in 2020. He is currently a graduate student at the College of Design and Innovation, Tongji University, Shanghai, China. His research interests encompass Human-AI Cooperation and wearable technology.Jialin YuanJialin Yuan received her Bachelor of Engineering degree from Tongji University, Shanghai, China, in 2022. She is currently a graduate student at the College of Design and Innovation, Tongji University, Shanghai, China. Her research interests tends toward Human-Computer Interaction.Xiaohua SunXiaohua Sun is a professor at the College of Design and Innovation, Tongji University, China. She received her PhD degree in Design and Computation from Massachusetts Institute of Technology in 2007. Her research interests include human-robot interaction (HRI), smart healthcare and rehabilitation, and extended reality (XR), etc.Bin YuBin Yu, Assistant Professor of Behavior Data Science at Nyenrode Business University. He formerly worked at Philips Design (2019–2022). He holds a PhD in Industrial Design (2018, TU/e) and an MS in Biomedical Engineering (2012, Northeastern University). His current research focuses on human-AI collaboration and data-driven behavior change.