This chapter presents digital human models (DHMs) as supporting tools in user-centered design for smart clothing. Smart clothing represents the new class of design era 2.0 with interactive technologies intended to be attractive, comfortable, and fit for the purpose of the identified user (Scataglini et al., 2019). While DHMs offer the possibility to analyze or predict human posture, motion, and other functions in a high-fidelity, physics-based, three-dimensional, real-time environment, smart clothing allows for monitoring vital (heart rate, breathing rate, temperature) and emotional (heart rate variability, electrodermal activity) parameters, movements, and postures. Effectiveness of functional wear is verified on the integration of DHMs into the design of a smart clothing system. Smart clothing represents a “second skin” that has a close “intimate” relation with the human body. This relation is physiological, psychological, biomechanical, and ergonomical. Physiological, biomechanical, and ergonomic aspects represent a retro-feedback of the user’s function in the iterative co-design workflow for designing smart garments, permitting the redesign and the technological refinement of it.

In this paper we present an innovative approach to design smart clothing using statistical body shape modeling (SBSM) from the CAESAR™ dataset. A combination of different digital technologies and applications are used to create a common co-design workflow for garment design. User and apparel product design and developers can get personalized prediction of cloth sizing, fitting and aesthetics.