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The suboptimal fit of a spacesuit can interfere with a crewmember's performance and is regarded as a potential risk factor for injury. To quantify suit fit, a virtual fit assessment model was previously developed to identify suit-to-body contact and interference using 3D human body scans and suit CAD models. However, ancillary suit components and garments worn inside of the suit have not been incorporated.

This study was conducted to predict a 3D model of the liquid cooling and ventilation garment (LCVG) from an arbitrary person's body scan. A total of 14 subjects were scanned in a scan wear and LCVG condition. A statistical model was generated using principal component analysis and random forest regression technique.

The model was able to predict the geometry of the LCVG layer at the accuracy of 5.3 cm maximum error and 1.7 cm root mean square error. The errors were more pronounced for the arms and lower torso, while the thighs and upper torso regions, which are critical for suit fit assessments, show more accurate predictions. A case study of suit fit with and without the LCVG model demonstrated that the new model can enhance the scope and accuracy of future spacesuit assessments.

The capabilities resulting from these modeling techniques would greatly expand the assessments of fit of the garment on various anthropometries. The results from this study can significantly improve the design process modeling and initial suit sizing efforts to optimize crew performance during extravehicular activity training and missions.