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.@en

In this paper, we present a new framework to integrate movement acquired by a motion capture system to a statistical body shape model using Blender. This provides a visualization of a digital human model based upon anthropometry and biomechanics of the subject. A moving statistical body shape model helps to visualize physical tasks with inter-individual variability in body shapes as well as anthropometric dimensions. This parametric modeling approach is useful for reliable prediction and simulation of the body shape movement of a specific population with a few given predictors such as stature, body mass index and age.@en

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.@en

Statistical body shape modeling (SBSM) is a well-known technique to map out the variability of body shapes and is commonly used in 3D anthropometric analyses. In this paper, a new approach to integrate movement acquired by a motion capture system with a body shape is proposed. This was done by selecting landmarks on a body shape model, and predicting a body shape based on features. Then, a virtual skeleton was generated relative to those landmarks. This skeleton was parented to a body shape, allowing to modify its pose and to add pre-recorded motion to different body shapes in a realistic way.@en