Smart soft scoliosis brace: Integrated textile sensors for real-time pressure monitoring
Adolescent idiopathic scoliosis (AIS) often requires bracing, but rigid designs can limit comfort and compliance. This study develops a soft scoliosis brace that integrates textile-based pressure sensors and a quantitative adjustment system to maintain consistent compressive forces. We hypothesize t...
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Published in | Textile research journal |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
29.04.2025
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Online Access | Get full text |
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Summary: | Adolescent idiopathic scoliosis (AIS) often requires bracing, but rigid designs can limit comfort and compliance. This study develops a soft scoliosis brace that integrates textile-based pressure sensors and a quantitative adjustment system to maintain consistent compressive forces. We hypothesize that our soft brace can achieve Cobb angle changes beyond the measurement error threshold, provide reliable pressure monitoring while maintaining flexibility, and provide force levels comparable to existing braces. We designed and fabricated a modular soft brace consisting of pelvic, lumbar, and thoracic bands, integrated textile-based piezoresistive sensors, and pressure-adjustable dials. Nine AIS subjects (10–17 years old, Cobb angles 20°–45°) wore the brace for one hour, and radiographs were taken before and after wear. Eight subjects demonstrated Cobb angle reductions of greater than 3°, with a median correction rate of 16.1% (range 6.8%–48.7%). Subjects reported improved motion and reduced self-consciousness compared to rigid braces. Integrated pressure sensors showed stable baseline resistance, repeatable measurements, and pressure distributions comparable to existing braces. Although immediate correction rates were lower than those achieved with rigid braces, the soft brace design improved comfort and compliance characteristics. Future research should examine long-term outcomes, personalize compression patterns, and incorporate wireless data transmission to optimize AIS treatment strategies. |
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ISSN: | 0040-5175 1746-7748 |
DOI: | 10.1177/00405175251325889 |