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Abstract Wearable strain sensors for movement tracking are a promising paradigm to improve clinical care for patients with neurological or musculoskeletal conditions, with further applicability to athletic wear, virtual reality, and next‐generation game controllers. Clothing‐like wearable strain sensors can support these use cases, as the fabrics used for clothing are generally lightweight and breathable, and interface with the skin in a manner that is mechanically and thermally familiar. Herein, a fabric capacitive strain sensor is presented and integrated into everyday clothing to measure human motions. The sensor is made of thin layers of breathable fabrics and exhibits high strains (>90%), excellent cyclic stability (>5000 cycles), and high water vapor transmission rates (≈50 g/h m2), the latter of which allows for sweat evaporation, an essential parameter of comfort. The sensor's functionality is verified under conditions similar to those experienced on the surface of the human body (35°C and % relative humidity) and after washing with fabric detergent. In addition, the fabric sensor shows stable capacitance at excitation frequencies up to 1 MHz, facilitating its low‐cost implementation in the Arduino environment. Finally, as a proof of concept, multiple fabric sensors are seamlessly integrated with commercial activewear to collect movement data. With the prioritization of breathability (air permeability and water vapor transmission), the fabric sensor design presented herein paves the way for future comfortable, unobtrusive, and discrete sensory clothing.
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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.
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- Award ID(s):
- 1842531
- PAR ID:
- 10285188
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 20
- Issue:
- 13
- ISSN:
- 1424-8220
- Page Range / eLocation ID:
- 3735
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s20133735
