Zhen Liang;Mengting Niu;Fangting Xie;Dongquan Zhang;Liyun Dai;Xiaohui Cai
{"title":"A Single-Ply and Knit-Only Textile Sensing Matrix for Mapping Body Surface Pressure","authors":"Zhen Liang;Mengting Niu;Fangting Xie;Dongquan Zhang;Liyun Dai;Xiaohui Cai","doi":"10.1109/JSEN.2024.3421320","DOIUrl":null,"url":null,"abstract":"Wearable sensing fabrics have great potential for applications such as human-computer interaction, motion monitoring, and human shape reconstruction. However, existing fabric sensors tend to sacrifice wearing comfort for sensing functionality, which restricts their application scenarios and hampers long-term usability due to poor wearability. To address this challenge, a novel fabric structure was designed to work with a flat knitting machine to realize a single-ply and knit-only textile pressure-sensing matrix. The sensing fabric has a sensing range of 0.255–35.65kPa, with a maximum sensitivity of 0.72kPa\n<inline-formula> <tex-math>$^{-{1}}$ </tex-math></inline-formula>\n. Although its sensing performance fluctuates under fatigue testing, washing and drying, folding, and stretching operations, it still supports use. It also has thermal comfort performance comparable to a regular T-shirt. We produced a pair of sensing shorts containing 256 sensing units and collected a total of 224483 frames of data containing 18 postures from six participants. Posture classification using ResNet-18 achieved 88.2% accuracy.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/10591632/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Wearable sensing fabrics have great potential for applications such as human-computer interaction, motion monitoring, and human shape reconstruction. However, existing fabric sensors tend to sacrifice wearing comfort for sensing functionality, which restricts their application scenarios and hampers long-term usability due to poor wearability. To address this challenge, a novel fabric structure was designed to work with a flat knitting machine to realize a single-ply and knit-only textile pressure-sensing matrix. The sensing fabric has a sensing range of 0.255–35.65kPa, with a maximum sensitivity of 0.72kPa
$^{-{1}}$
. Although its sensing performance fluctuates under fatigue testing, washing and drying, folding, and stretching operations, it still supports use. It also has thermal comfort performance comparable to a regular T-shirt. We produced a pair of sensing shorts containing 256 sensing units and collected a total of 224483 frames of data containing 18 postures from six participants. Posture classification using ResNet-18 achieved 88.2% accuracy.
期刊介绍:
The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following:
-Sensor Phenomenology, Modelling, and Evaluation
-Sensor Materials, Processing, and Fabrication
-Chemical and Gas Sensors
-Microfluidics and Biosensors
-Optical Sensors
-Physical Sensors: Temperature, Mechanical, Magnetic, and others
-Acoustic and Ultrasonic Sensors
-Sensor Packaging
-Sensor Networks
-Sensor Applications
-Sensor Systems: Signals, Processing, and Interfaces
-Actuators and Sensor Power Systems
-Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting
-Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data)
-Sensors in Industrial Practice