Modeling Fabric Movement for Future E-Textile Sensors.

Studies with e-textile sensors embedded in garments are typically performed on static and controlled phantom models that do not reflect the dynamic nature of wearables. Instead, our objective was to understand the noise e-textile sensors would experience during real-world scenarios. Three types of sleeves, made of loose, tight, and stretchy fabrics, were applied to a phantom arm, and the corresponding fabric movement was measured in three dimensions using physical markers and image-processing software. Our results showed that the stretchy fabrics allowed for the most consistent and predictable clothing-movement (average displacement of up to -2.3 ± 0.1 cm), followed by tight fabrics (up to -4.7 ± 0.2 cm), and loose fabrics (up to -3.6 ± 1.0 cm). In addition, the results demonstrated better performance of higher elasticity (average displacement of up to -2.3 ± 0.1 cm) over lower elasticity (average displacement of up to -3.8 ± 0.3 cm) stretchy fabrics. For a case study with an e-textile sensor that relies on wearable loops to monitor joint flexion, our modeling indicated errors as high as 65.7° for stretchy fabric with higher elasticity. The results from this study can (a) help quantify errors of e-textile sensors operating "in-the-wild," (b) inform decisions regarding the optimal type of clothing-material used, and (c) ultimately empower studies on noise calibration for diverse e-textile sensing applications.