Agreement of anthropometric and body composition measures predicted from 2D smartphone images and body impedance scales with criterion methods.

Background/objectives: Body composition and anthropometry assessment from two-dimensional smartphone images is possible through advancement of computational hardware and artificial intelligence (AI) techniques. This study established agreement of a novel smartphone assessment, compared with traditional bioelectrical impedance analysis (BIA), and criterion measures.

Subjects/methods: Body composition of 929 adults was measured using DXA (GE lunar iDXA), a foot-to-foot BIA machine (TANITA BC-313), and predictions from two-dimensional smartphone images.

A new validation technique for estimations of body segment inertia tensors: Principal axes of inertia do matter.

The aims of this study were to: (i) establish a new criterion method to validate inertia tensor estimates by setting the experimental angular velocity data of an airborne objects as ground truth against simulations run with the estimated tensors, and (ii) test the sensitivity of the simulations to changes in the inertia tensor components. A rigid steel cylinder was covered with reflective kinematic markers and projected through a calibrated motion capture volume. Simulations of the airborne motion were run with two models, using inertia tensor estimated with geometric formula or the compound pendulum technique.

Body Segment Inertial Parameters of elite swimmers Using DXA and indirect Methods.

As accurate body segment inertial parameters (BSIPs) are difficult to obtain in motion analysis, this study computed individual BSIPs from DXA scan images. Therefore, by co-registering areal density data with DXA grayscale image, the relationship between pixel color gradient and the mass within the pixel area could be established. Thus, one can calculate BSIPs, including segment mass, center of mass (COM) and moment of inertia about the sagittal axis (Ixx).

A High Resolution Color Image Restoration Algorithm for Thin TOMBO Imaging Systems.

In this paper, we present a blind image restoration algorithm to reconstruct a high resolution (HR) color image from multiple, low resolution (LR), degraded and noisy images captured by thin (< 1mm) TOMBO imaging systems. The proposed algorithm is an extension of our grayscale algorithm reported in [1] to the case of color images. In this color extension, each Point Spread Function (PSF) of each captured image is assumed to be different from one color component to another and from one imaging unit to the other.