Child body shape measurement using depth cameras and a statistical body shape model

Abstract

We present a new method for rapidly measuring child body shapes from noisy, incomplete data captured from low-cost depth cameras. This method fits the data using a statistical body shape model (SBSM) to find a complete avatar in the realistic body shape space. The method also predicts a set of standard anthropometric data for a specific subject without measuring dimensions directly from the fitted model. Since the SBSM was developed using principal component (PC) analysis, we formulate an optimisation problem to fit the model in which the degrees of freedom are defined in PC-score space. The mean unsigned distance between the fitted-model based on depth-camera data and the high-resolution laser scan data was 9.4 mm with a standard deviation (SD) of 5.1 mm. For the torso, the mean distance was 2.9 mm (SD 1.4 mm). The correlations between standard anthropometric dimensions predicted by the SBSM and manually measured dimensions exceeded 0.9.

Abstract

Practitioner Summary: Rapid and robust body shape measurement is beneficial for tracking child body shapes and anthropometric changes. A custom avatar generated by rapidly fitting a statistical body shape model to noisy scan data showed the potential for good accuracy in measuring child body shape.

Keywords::

• child body shape measurement
• statistical body shape model
• depth camera
• surface fitting
• anthropometry

Additional information

Funding

This research was funded in part by the MCubed program at the University of Michigan. The child SBSM was developed with funding from the National Highway Traffic Safety Administration.