Textile Knitted Stretch Sensors for Wearable Health Monitoring: Design and Performance Evaluation

Biosensors (Basel). 2022 Dec 27;13(1):34. doi: 10.3390/bios13010034.

Abstract

The advancement of smart textiles has led to significant interest in developing wearable textile sensors (WTS) and offering new modalities to sense vital signs and activity monitoring in daily life settings. For this, textile fabrication methods such as knitting, weaving, embroidery, and braiding offer promising pathways toward unobtrusive and seamless sensing for WTS applications. Specifically, the knitted sensor has a unique intermeshing loop structure which is currently used to monitor repetitive body movements such as breathing (microscale motion) and walking (macroscale motion). However, the practical sensing application of knit structure demands a comprehensive study of knit structures as a sensor. In this work, we present a detailed performance evaluation of six knitted sensors and sensing variation caused by design, sensor size, stretching percentages % (10, 15, 20, 25), cyclic stretching (1000), and external factors such as sweat (salt-fog test). We also present regulated respiration (inhale-exhale) testing data from 15 healthy human participants; the testing protocol includes three respiration rates; slow (10 breaths/min), normal (15 breaths/min), and fast (30 breaths/min). The test carried out with statistical analysis includes the breathing time and breathing rate variability. These testing results offer an empirically derived guideline for future WTS research, present aggregated information to understand the sensor behavior when it experiences a different range of motion, and highlight the constraints of the silver-based conductive yarn when exposed to the real environment.

Keywords: e-textiles; knitted sensors; smart textiles; stretch sensors; wearable health sensors.

MeSH terms

  • Humans
  • Motion
  • Movement
  • Respiration*
  • Textiles
  • Wearable Electronic Devices*

Grants and funding

This material is based upon work supported by the National Science Foundation under Grant 2139724 and the Rhode Island Commerce Innovation Voucher.