Simulation-Based Contact Pressure Prediction Model to Optimize Health Monitoring Using E-Textile Integrated Garment

Advancements in wearable technology have integrated textile sensors into garments for long-term electrocardiogram (ECG) monitoring. However, optimizing biosignal quality, motion artifacts, and wearer comfort in electronic textiles (E-textiles) remains challenging. While designing appropriate contact pressure (CP) is crucial, there is a lack of guidance on proper material selection and sizing for achieving the desired CP. This article presents a novel CP prediction model that utilizes three-dimensional garment simulation (3DGS) to optimize knit textiles for health monitoring. First, a stress test method is devised in the simulator to examine the reliability of simulated stress. Based on understanding the simulated stress mechanism, the CP model is developed using simulation parameters. The model is validated against experimental CP values, exhibiting high accuracy (
${R}^{{2}}= {0.9}$
). The effectiveness of the CP model is validated through the demonstration of a customized ECG armband incorporating screen-printed dry electrodes on knit fabrics. Analyzing ECG signals, CP, and applied strains validates the benefits of strategically selected materials and sizing. Specifically, the knit sample with 90% polyester and 10% spandex (S-10) for the 15%–20% range and the knit sample with 85% polyester and 18% spandex (S-18) for the 10%–15% strain range significantly enhance ECG quality, resulting in higher signal-to-noise ratios (SNR) of 33.45 (±1.72) and 34.57 (±0.84)−36.61(±1.81), respectively. These design parameters achieve the desired CP range of 1–1.5 kPa, optimizing the functionality and comfort of the ECG armband. The CP model sets a benchmark for the strategic manufacturing of health monitoring garments by integrating digital technology.