A garment-integrated textile stitch-based strain sensor device, IoT-Enabled for enhanced wearable sportswear applications

The field of wearable technology is undergoing a transformative shift with the integration of IoT-enabled, AI-assisted sensors into sportswear, bringing sophisticated performance analysis within reach for a broader audience. Despite advancements in stitch strain sensors, there is a significant research gap in developing high-performance, stable, and IoT-connected complete stitch strain wearable sensor systems. These advanced sensors are essential for enhancing wearable technology and meeting the growing demands for improved user experience and functionality. In this study by utilizing conductive sewing threads and innovative stitching techniques, we fabricated textile strain sensors that offer high sensitivity, flexibility, and durability. Extensive calibration tests revealed strong linear relationships between resistance and tensile strain, with highest sensor sensitivity (2.08 Ω/mm during loading and 2.72 Ω/mm during unloading). These sensors demonstrate reliable performance with minimal hysteresis, particularly in shorter lengths. The wearable sportswear device, tested in gym environments, demonstrated the sensor's capability to provide real-time feedback on knee flexing/bending and body movement during exercise. This functionality supports injury prevention and enhances workout efficiency. The sensor system is IoT-enabled, powered by an ESP8266 microcontroller, facilitating data transmission to the Thing Speak platform for advanced analysis and remote monitoring. This comprehensive approach not only advances wearable technology but also opens new avenues for remote health monitoring and smart e-textiles, contributing significantly to the fields of sports science and fitness technology. • Transformative Integration: IoT-enabled, AI-assisted sensors in sportswear revolutionize performance analysis for a wider audience. • Innovative Sensor Development: Developed a cost-effective, user-friendly, textile-based strain sensor system for real-time athletic performance monitoring. • High Sensitivity and Durability: Created sensors using conductive sewing threads and innovative stitching, exhibiting high sensitivity (2.08818 Ω/mm loading, 2.72727 Ω/mm unloading), flexibility, and durability. • Real-time Feedback: Tested in gym environments, the wearable device provided real-time feedback on bending and movement, aiding in injury prevention and enhancing workout efficiency. • Advanced Data Transmission: IoT-enabled system with ESP8266 microcontroller and Thing Speak platform facilitated advanced data analysis and remote monitoring.

Authors

Institutions

Publication Details

Journal
Results in Engineering
Published
2024-08-30
DOI
https://doi.org/10.1016/j.rineng.2024.102794
Citations
13
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
2.40

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A garment-integrated textile stitch-based strain sensor device, IoT-Enabled for enhanced wearable sportswear applications

Orathai Tangsirinaruenart, George K. Stylios, Danying Yang, Akshaya Kumar Aliyana
13 citations
Results in Engineering
Advanced Sensor and Energy Harvesting Materials
2.40
article

A garment-integrated textile stitch-based strain sensor device, IoT-Enabled for enhanced wearable sportswear applications

Orathai Tangsirinaruenart, George K. Stylios, Danying Yang, Akshaya Kumar Aliyana
article en
13 citations

Abstract

The field of wearable technology is undergoing a transformative shift with the integration of IoT-enabled, AI-assisted sensors into sportswear, bringing sophisticated performance analysis within reach for a broader audience. Despite advancements in stitch strain sensors, there is a significant research gap in developing high-performance, stable, and IoT-connected complete stitch strain wearable sensor systems. These advanced sensors are essential for enhancing wearable technology and meeting the growing demands for improved user experience and functionality. In this study by utilizing conductive sewing threads and innovative stitching techniques, we fabricated textile strain sensors that offer high sensitivity, flexibility, and durability. Extensive calibration tests revealed strong linear relationships between resistance and tensile strain, with highest sensor sensitivity (2.08 Ω/mm during loading and 2.72 Ω/mm during unloading). These sensors demonstrate reliable performance with minimal hysteresis, particularly in shorter lengths. The wearable sportswear device, tested in gym environments, demonstrated the sensor's capability to provide real-time feedback on knee flexing/bending and body movement during exercise. This functionality supports injury prevention and enhances workout efficiency. The sensor system is IoT-enabled, powered by an ESP8266 microcontroller, facilitating data transmission to the Thing Speak platform for advanced analysis and remote monitoring. This comprehensive approach not only advances wearable technology but also opens new avenues for remote health monitoring and smart e-textiles, contributing significantly to the fields of sports science and fitness technology. • Transformative Integration: IoT-enabled, AI-assisted sensors in sportswear revolutionize performance analysis for a wider audience. • Innovative Sensor Development: Developed a cost-effective, user-friendly, textile-based strain sensor system for real-time athletic performance monitoring. • High Sensitivity and Durability: Created sensors using conductive sewing threads and innovative stitching, exhibiting high sensitivity (2.08818 Ω/mm loading, 2.72727 Ω/mm unloading), flexibility, and durability. • Real-time Feedback: Tested in gym environments, the wearable device provided real-time feedback on bending and movement, aiding in injury prevention and enhancing workout efficiency. • Advanced Data Transmission: IoT-enabled system with ESP8266 microcontroller and Thing Speak platform facilitated advanced data analysis and remote monitoring.

Results in EngineeringVol. 23
Rajamangala University of Technology (TH), Heriot-Watt University (GB)
Engineering and Physical Sciences Research Council
Openalex Percentile: Top 11%
Advanced Sensor and Energy Harvesting Materials
2.40
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.