Byoung Keon Park

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

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. 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.

Parametric body shape model of standing children aged 3–11 years

A statistical body shape model (SBSM) for children was developed for generating a child body shape with desired anthropometric parameters. A standardised template mesh was fit to whole-body laser scan data from 137 children aged 3–11 years. The mesh coordinates along with a set of surface landmarks and 27 manually measured anthropometric variables were analysed using principal component (PC) analysis. PC scores were associated with anthropometric predictors such as stature, body mass index (BMI) and ratio of erect sitting height to stature (SHS) using a regression model. When the original scan data were compared with the predictions of the SBSM using each subjects stature, BMI and SHS, the mean absolute error was 10.4 ± 5.8 mm, and 95th percentile error was 24.0 ± 18.5 mm. The model, publicly available online, will have utility for a wide range of applications. Practitioner Summary: A statistical body shape model for children helps to account for inter-individual variability in body shapes as well as anthropometric dimensions. This parametric modelling approach is useful for reliable prediction of the body shape of a specific child with a few given predictors such as stature, body mass index and age.

A Statistical Skull Geometry Model for Children 0-3 Years Old

Head injury is the leading cause of fatality and long-term disability for children. Pediatric heads change rapidly in both size and shape during growth, especially for children under 3 years old (YO). To accurately assess the head injury risks for children, it is necessary to understand the geometry of the pediatric head and how morphologic features influence injury causation within the 0–3 YO population. In this study, head CT scans from fifty-six 0–3 YO children were used to develop a statistical model of pediatric skull geometry. Geometric features important for injury prediction, including skull size and shape, skull thickness and suture width, along with their variations among the sample population, were quantified through a series of image and statistical analyses. The size and shape of the pediatric skull change significantly with age and head circumference. The skull thickness and suture width vary with age, head circumference and location, which will have important effects on skull stiffness and injury prediction. The statistical geometry model developed in this study can provide a geometrical basis for future development of child anthropomorphic test devices and pediatric head finite element models.

Function-based morphing methodology for parameterizing patient-specific models of human proximal femurs☆

Abstract This paper presents a novel morphing method for parameterizing patient-specific femur models based on femoral biomechanical functions. The proposed function-based morphing (FBM) method aims to provide a robust way to independently morph each partial functional region of the target femur structure by simply assigning the given functional parameters such as the femoral head diameter, neck length and diameter, and neck inclination angle. FBM includes three steps: (1) feature recognition to segment a femoral model into functional regions, (2) simplification to estimate the original parameters of the model and to define the morphing criteria as geometrical constraints, and (3) morphing to obtain the required shape by applying FBM fields that convert the terms of parametric changes into morphing vector terms for each segmented region. The proposed method was validated on a total of 48 patient-specific femur models. These models were parameterized and morphed without unexpected parametric changes, and the averaged error between the required parameters and the re-estimated parameters after morphing was 3.47%. Our observations indicate that the variation models developed in this study can be used as fundamentals for various functional sensitivity analyses for predicting changes in biomechanical responses due to the morphological changes of a subject-specific femur structure.

A parametric model of child body shape in seated postures

ABSTRACT Objective: The shape of the current physical and computational surrogates of children used for restraint system assessments is based largely on standard anthropometric dimensions. These scalar dimensions provide valuable information on the overall size of the individual but do not provide good guidance on shape or posture. This study introduced the development of a parametric model that statistically predicts individual child body shapes in seated postures with a few given parameters. Methods: Surface geometry data from a laser scanner of children ages 3 to 11 (n = 135) were standardized by a 2-level fitting method using intermediate templates. The standardized data were analyzed by principal component analysis (PCA) to efficiently describe the body shape variance. Parameters such as stature, body mass index, erect sitting height, and 2 posture variables related to torso recline and lumbar spine flexion were associated with the PCA model using regression. Results: When the original scan data were compared with the predictions of the model using the given subject dimensions, the average root mean square error for the torso was 9.5 mm, and the 95th percentile error was 17.35 mm. Conclusions: For the first time, a statistical model of child body shapes in seated postures is available. This parametric model allows the generation of an infinite number of virtual children spanning a wide range of body sizes and postures. The results have broad applicability in product design and safety analysis. Future work is needed to improve the representation of hands and feet and to extend the age range of the model. The model presented in this article is publicly available online through HumanShape.org.

Discussion panel on computer vision and occupational ergonomics

Computer vision has already impacted a diverse field of applications, ranging from industrial robotics, intelligent and autonomous vehicles, security surveillance, manufacturing inspection, and human-computer interaction. Furthermore, digital imaging technologies are advancing ever smaller in size, finer in granularity, and faster in processing, while becoming less expensive and thus more accessible to businesses, organizations, and individuals in devices such as smart phones and tablets. Consumer products such as the Kinect™ offer advanced marker-less 3D motion capture capabilities at a low cost. New computer vision methods are now being researched and developed for occupational ergonomics applications. It is anticipated that these new tools will profoundly impact the future of occupational ergonomics and provide a variety of new instruments and techniques for design, analysis and evaluation in the practice of ergonomics. A panel of leading experts will describe some of the cutting edge research they are pursuing utilizing computer vision for occupational ergonomics applications. Radwin, Lee and Li use algorithms that track pixel patterns recorded from conventional video for quantifying repetitive hand motion, manual lifting and whole-body activities. Lee and Li describe the use computer vision tools to predict joint angles for a whole-body link model. Lieblich and Park describe the use of Kinect™ for classifying postures and generating individualized task specific avatars. Each of these approaches has specific advantages and limitations, which will be addressed by the panel. A discussion will follow exploring future research needs as well as engaging in a discussion among panelists and attendees about the needs, limitations, and obstacles that this new technology faces in bringing it into practice.