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Clothing systems have been successfully used as a viable embedding media for several vital signals monitoring systems, making them smart garments. Two persistent issues with smart garments are improper fit and wearing discomfort. When collecting biometric data, precise sensor placement is extremely important, and when a garment is not comfortable, it risks not being used at all. Conventionally, fit is often assessed using fit models, either human subjects or static mannequins. Most companies determine fit model sizes that represent a median population, and the garments developed thus cannot be scaled directly to an obese body, which now represents a significant portion of the US population. In this study, fit models representing the obese population were selected from the CAESAR scan database for male and female and a male body form was additively manufactured using 3D printing technique. Digital scans of 185 obese male and 204 obese female from the CAESAR database were used to select the appropriate fit models. The bivariate distribution of chest and waist-front length measurements determined a representative subpopulation of 15 male cases and 14 female cases. Furthermore, sagittal cross section curvature analysis was performed on the cases to capture profile variations. Subsequently, the anthropometric distance variations among the cases for several key landmarks were measured to establish a range for sensor placement on the smart garment. From the 15 male cases, one subject was heuristically identified as the final fit model. The identified scan was preprocessed and torso region of the scan was extracted using the Polyworks V14 software. In order to make a life-like replication of the torso, two 3D printers were simultaneously used to print the model as 16 individual hollow slices. Subsequently, the slices were assembled together to produce the full scale obese-fit-model 3D form.

Developing a 3D-Printed Obese Model for Assessing Fit of Wearable Smart Garments