Groundbreaking anthropometric method to precisely measure tissue deformation
Soft tissue deformation during body movement has long posed a challenge to achieving optimal garment fit and comfort, particularly in sportswear and functional medical wear. Researchers at The Hong Kong Polytechnic University (PolyU) have developed a novel anthropometric method that delivers highly accurate measurements to enhance the performance and design of compression-based apparel.
Prof. Joanne Yip, Associate Dean and Professor of the School of Fashion and Textiles at PolyU, and her research team pioneered this anthropometric method using image recognition algorithms to systematically access tissue deformation while minimising motion-related errors. The team also developed an analytical model to predict tissue deformation using the Boussinesq solution, based on elastic theory and stress function methodology. By leveraging image recognition algorithms, this innovation quantifies tissue deformation during movement, addressing a longstanding challenge in sportswear and wearable tech design.
Inaccurate deformation measurements, especially during motion, often lead to ill-fitting designs that undermine functionality. This innovative approach tackles the issue by minimising motion artifacts and providing a systematic framework to correlate garment pressure with tissue response, which is vital for optimising wearables’ the biochemical efficacy.
Soft tissue deformation is a critical factor directly influencing appearance, comfort, performance, and physiological effects such as blood circulation and muscle support. With the integration of mechanical property testing, the method accurately predicts tissue deformation. Validation against body scanning measurements showed deviations within 1.15 mm under static condition and 2.36 mm in dynamic condition. The remarkable precision of this method equips designers with reliable data that accurately reflects soft tissue deformation.
Prof. Joanne Yip said, “Our technology is highly adaptable to compression-based garments, including sportswear such as leggings and functional medical wear like compression stockings and post-surgical garments. The analytical model can be tailored to different garment types by adjusting parameters like material mechanical properties and circumferential dimensions.”
Sports leggings with different material mechanical properties, pattern designs and circumferential dimensions were used as experimental samples. Research findings offer actionable insights that link material properties to garment fit and performance. This framework not only advances biomechanical simulation techniques for wearable applications but also provides a practical tool for optimising sportswear ergonomics, enabling data-driven design of compression garments that enhances athletic performance while preventing the risk of musculoskeletal injuries.
This innovative technology holds promising transformative potential for the industry, offering feasible and cost-effective applications. It can be integrated into existing CAD/CAM system to streamline prototyping and reduce reliance on trial-and-error filling. By quantifying individual tissue response, this technique supports personalised garment design, particularly beneficial for medical compression wear tailored to specific patient needs. Additionally, the image-based tools reduce dependence on expensive motion-capture systems, making the approach accessible for small and medium-sized enterprises.
The research has been published in a paper titled “A novel anthropometric method to accurately evaluate tissue deformation” in the academic journal Frontiers in Bioengineering and Biotechnology.
This technology breakthrough underscores PolyU excellence in interdisciplinary translational research, integrating its strengths in fashion, biomechanics, materials science, computing, and engineering to solve real-world compression sportswear design and wearable design challenges.
Prof. Joanne Yip, Associate Dean and Professor of the School of Fashion and Textiles at PolyU, and her research team pioneered this anthropometric method using image recognition algorithms to systematically access tissue deformation while minimising motion-related errors. The team also developed an analytical model to predict tissue deformation using the Boussinesq solution, based on elastic theory and stress function methodology. By leveraging image recognition algorithms, this innovation quantifies tissue deformation during movement, addressing a longstanding challenge in sportswear and wearable tech design.
Inaccurate deformation measurements, especially during motion, often lead to ill-fitting designs that undermine functionality. This innovative approach tackles the issue by minimising motion artifacts and providing a systematic framework to correlate garment pressure with tissue response, which is vital for optimising wearables’ the biochemical efficacy.
Soft tissue deformation is a critical factor directly influencing appearance, comfort, performance, and physiological effects such as blood circulation and muscle support. With the integration of mechanical property testing, the method accurately predicts tissue deformation. Validation against body scanning measurements showed deviations within 1.15 mm under static condition and 2.36 mm in dynamic condition. The remarkable precision of this method equips designers with reliable data that accurately reflects soft tissue deformation.
Prof. Joanne Yip said, “Our technology is highly adaptable to compression-based garments, including sportswear such as leggings and functional medical wear like compression stockings and post-surgical garments. The analytical model can be tailored to different garment types by adjusting parameters like material mechanical properties and circumferential dimensions.”
Sports leggings with different material mechanical properties, pattern designs and circumferential dimensions were used as experimental samples. Research findings offer actionable insights that link material properties to garment fit and performance. This framework not only advances biomechanical simulation techniques for wearable applications but also provides a practical tool for optimising sportswear ergonomics, enabling data-driven design of compression garments that enhances athletic performance while preventing the risk of musculoskeletal injuries.
This innovative technology holds promising transformative potential for the industry, offering feasible and cost-effective applications. It can be integrated into existing CAD/CAM system to streamline prototyping and reduce reliance on trial-and-error filling. By quantifying individual tissue response, this technique supports personalised garment design, particularly beneficial for medical compression wear tailored to specific patient needs. Additionally, the image-based tools reduce dependence on expensive motion-capture systems, making the approach accessible for small and medium-sized enterprises.
The research has been published in a paper titled “A novel anthropometric method to accurately evaluate tissue deformation” in the academic journal Frontiers in Bioengineering and Biotechnology.
This technology breakthrough underscores PolyU excellence in interdisciplinary translational research, integrating its strengths in fashion, biomechanics, materials science, computing, and engineering to solve real-world compression sportswear design and wearable design challenges.
