Longitudinal concordance of body composition and anthropometric assessment by a novel smartphone application across a 12-week self-managed weight loss intervention.

Smartphone applications (SPA) now offer the ability to provide accessible in-home monitoring of relevant individual health biomarkers. Previous cross-sectional validations of similar technologies have reported acceptable accuracy with high-grade body composition assessments; this research assessed longitudinal agreement of a novel SPA across a self-managed weight loss intervention of thirty-eight participants (twenty-one males, seventeen females). Estimations of body mass (BM), body fat percentage (BF%), fat-free mass (FFM) and waist circumference (WC) from the SPA were compared with ground truth (GT) measures from a dual-energy X-ray absorptiometry scanner and expert technician measurement. Small mean differences (MD) and standard error of estimate (SEE) were observed between method deltas (ΔBM: MD = 0·12 kg, SEE = 2·82 kg; ΔBF%: MD = 0·06 %, SEE = 1·65 %; ΔFFM: MD = 0·17 kg, SEE = 1·65 kg; ΔWC: MD = 1·16 cm, SEE = 2·52 cm). Concordance correlation coefficient (CCC) assessed longitudinal agreement between the SPA and GT methods, with moderate concordance (CCC: 0·55-0·73) observed for all measures. The novel SPA may not be interchangeable with high-accuracy medical scanning methods yet offers significant benefits in cost, accessibility and user comfort, in conjunction with the ability to monitor body shape and composition estimates over time.

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. The deviation angles between experimental (ground truth) and simulated angular velocity vectors and the root mean squared deviation angle were computed for every simulation. Monte Carlo analyses were performed to assess the sensitivity of simulations to changes in magnitude of principal moments of inertia within ±10% and to changes in orientation of principal axes of inertia within ±10° (of the geometric-based inertia tensor). Root mean squared deviation angles ranged between 2.9° and 4.3° for the inertia tensor estimated geometrically, and between 11.7° and 15.2° for the compound pendulum values. Errors up to 10% in magnitude of principal moments of inertia yielded root mean squared deviation angles ranging between 3.2° and 6.6°, and between 5.5° and 7.9° when lumped with errors of 10° in principal axes of inertia orientation. The proposed technique can effectively validate inertia tensors from novel estimation methods of body segment inertial parameter. Principal axes of inertia orientation should not be neglected when modelling human/animal mechanics.

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). This technique calculated whole body mass very accurately (%RMSE of < 1.5%) relatively to results of the generic DXA scanner software. The BSIPs of elite male and female swimmers, and young adult Caucasian males (n = 28), were computed using this DXA method and 5 other common indirect estimation methods. A 3D surface scan of each subject enabled mapping of key anthropometric variables required for the 5 indirect estimation methods. Mass, COM and Ixx were calculated for seven body segments (head, trunk, head + trunk, upper arm, forearm, thigh and shank). Between-group comparisons of BSIPs revealed that elite female swimmers had the lowest segment masses of the three groups (p < 0.05). Elite male swimmers recorded the greatest inertial parameters of the trunk and upper arms (p < 0.05). Using the DXA method as the criterion, the five indirect methods produced errors greater than 10% for at least one BSIP in all three populations. Therefore, caution is required when computing BSIPs for elite swimmers via these indirect methods, DXA accurately estimated BSIPs in the frontal plane. Key PointsElite swimmers have significantly different body segment inertial parameters than young adult Caucasian males.The errors computed from indirect BSIP estimation methods are large regardless whether applied to elite swimmers or young adult Caucasian males.No indirect estimation method consistently performed best.

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. For the task of image restoration, we use all spectral information in each captured image to restore each output pixel in the reconstructed HR image, i.e., we use the most efficient global category of point operations. First, the composite RGB color components of each captured image are extracted. A blind estimation technique is then applied to estimate the spectra of each color component and its associated blurring PSF. The estimation process is formed in a way that minimizes significantly the interchannel cross-correlations and additive noise. The estimated PSFs together with advanced interpolation techniques are then combined to compensate for blur and reconstruct a HR color image of the original scene. Finally, a histogram normalization process adjusts the balance between image color components, brightness and contrast. Simulated and experimental results reveal that the proposed algorithm is capable of restoring HR color images from degraded, LR and noisy observations even at low Signal-to-Noise Energy ratios (SNERs). The proposed algorithm uses FFT and only two fundamental image restoration constraints, making it suitable for silicon integration with the TOMBO imager.

Spectral-Based Blind Image Restoration Method for Thin TOMBO Imagers.

With the recent advances in microelectronic fabrication technology, it becomes now possible to fabricate thin imagers, less than half a millimeter thick. Dubbed TOMBO (an acronym for thin observation module by bound optics), a thin camera-on-a-chip integrates micro-optics and photo-sensing elements, together with advanced processing circuitry, all on a single silicon chip. Modeled after the compound-eye found in insects and many other arthropods, the TOMBO imager captures simultaneously a mosaic of low resolution images. In this paper, we describe and analyze a novel spectral-based blind algorithm that enables the restoration of a high resolution image from the captured low resolution images.The proposed blind restoration method does not require prior information about the imaging system nor the original scene. Furthermore, it alleviates the need for conventional de-shading and rearrangement processing techniques. Experimental results demonstrate that the proposed method can restore images for SNER lower than 3dB.