KinectFusion is a typical three-dimensional reconstruction technique which enables generation of individual three-dimensional human models from consumer depth cameras for understanding body shapes. The aim of this study was to compare three-dimensional reconstruction results obtained using KinectFusion from data collected with two different types of depth camera (time-of-flight and stereoscopic cameras) and compare these results with those of a commercial three-dimensional scanning system to determine which type of depth camera gives improved reconstruction. Torso mannequins and machined aluminium cylinders were used as the test objects for this study. Two depth cameras, Microsoft Kinect V2 and Intel Realsense D435, were selected as the representatives of time-of-flight and stereoscopic cameras, respectively, to capture scan data for the reconstruction of three-dimensional point clouds by KinectFusion techniques. The results showed that both time-of-flight and stereoscopic cameras, using the developed rotating camera rig, provided repeatable body scanning data with minimal operator-induced error. However, the time-of-flight camera generated more accurate three-dimensional point clouds than the stereoscopic sensor. Thus, this suggests that applications requiring the generation of accurate three-dimensional human models by KinectFusion techniques should consider using a time-of-flight camera, such as the Microsoft Kinect V2, as the image capturing sensor.
The purpose of this study was to determine the kinematic patterns that maximized the vertical force produced during the water polo eggbeater kick. Twelve water polo players were tested executing the eggbeater kick with the trunk aligned vertically and with the upper limbs above water while trying to maintain as high a position as possible out of the water for nine eggbeater kick cycles. Lower limb joint angular kinematics, pitch angles and speed of the feet were calculated. The vertical force produced during the eggbeater kick cycle was calculated using inverse dynamics for the independent lower body segments and combined upper body segments, and a participant-specific second degree regression equation for the weight and buoyancy contributions. Vertical force normalized to body weight was associated with hip flexion (Average, r=0.691; Maximum, r=0.791; Range of Motion, r=0.710), hip abduction (Maximum, r=0.654), knee flexion (Average, r=0.716; Minimum, r=0.653) and knee flexion-extension angular velocity (r=0.758). Effective orientation of the hips resulted in fast horizontal motion of the feet with positive pitch angles. Vertical motion of the feet was negatively associated with vertical force. A multiple regression model comprising the non-collinear variables of maximum hip abduction, hip flexion range of motion and knee flexion angular velocity accounted for 81% of the variance in normalized vertical force. For high performance in the water polo eggbeater kick players should execute fast horizontal motion with the feet by having large abduction and flexion of the hips, and fast extension and flexion of the knees. Word Count: 2900 3
Somatotype is an approach to quantify body physique (shape and body composition). Somatotyping by manual measurement (the anthropometric method) or visual rating (the photoscopic method) needs technical expertize to minimize intra-and inter-observer errors. This study aims to develop machine learning models which enable automatic estimation of Heath-Carter somatotypes using a single-camera 3D scanning system. Single-camera 3D scanning was used to obtain 3D imaging data and computer vision techniques to extract features of body shape. Machine learning models were developed to predict participants' somatotypes from the extracted shape features. These predicted somatotypes were compared against manual measurement procedures. Data were collected from 46 participants and used as the training/validation set for model developing, whilst data collected from 17 participants were used as the test set for model evaluation. Evaluation tests showed that the 3D scanning methods enable accurate (mean error < 0.5; intraclass correlation coefficients >0.8) and precise (test-retest root mean square error < 0.5; intraclass correlation coefficients >0.8) somatotype predictions. This study shows that the 3D scanning methods could be used as an alternative to traditional somatotyping approaches after the current models improve with the large datasets.
Three-dimensional (3D) photonic scanning is an emerging technique to acquire accurate body segment parameter data. This study established the repeated reliability of segmental centres of mass when using 3D photonic scanning (3DPS). Seventeen male participants were scanned twice by a 3D whole-body laser scanner. The same operators conducted the reconstruction and segmentation processes to obtain segmental meshes for calculating the segmental centres of mass. The segmental centres of mass obtained from repeated 3DPS were compared by relative technical error of measurement (TEM). Hypothesis tests were conducted to determine the size of change required for each segment to be determined a true variation. The relative TEMs for all segments were less than 5%. The relative changes in centres of mass at ±1.5% for most segments can be detected (p < 0.05). The arm segments which are difficult to keep in the same scanning pose generated more error than other segments. Practitioner Summary: Three-dimensional photonic scanning is an emerging technique to acquire body segment parameter data. This study established the repeated reliability of segmental centres of mass when using 3D photonic scanning and emphasised that the error for arm segments need to be considered while using this technique to acquire centres of mass.
Whole-body volumes and segmental volumes are highly related to the health and medical condition of individuals. However, the traditional manual post-processing of raw 3D scanned data is time-consuming and needs technical expertise. The purpose of this study was to develop bespoke software for obtaining whole-body volumes and segmental volumes from raw 3D scanned data automatically and to establish its accuracy and reliability. The bespoke software applied Stitched Puppet model fitting techniques to deform template models to fit the 3D raw scanned data to identify the segmental endpoints and determine their locations. Finally, the bespoke software used the location information of segmental endpoints to set segmental boundaries on the reconstructed meshes and to calculate body volume. The whole-body volumes and segmental volumes (head& neck, torso, arms, and legs) of 29 participants processed by the traditional manual operation were regarded as the references and compared to the measurements obtained with the bespoke software using the intra-method and inter-method relative technical errors of measurement. The results showed that the errors in whole-body volumes and most segmental volumes acquired from the bespoke software were less than 5%. Overall, the bespoke software developed in this study can complete the post-processing tasks without any technical expertise, and the obtained whole-body volumes and segmental volumes can achieve good accuracy for some applications in health and medicine.
The elliptical zone method (E-Zone) can be used to obtain reliable body volume data including total body volume and segmental volumes with inexpensive and portable equipment. The purpose of this research was to assess the accuracy of body volume data obtained from E-Zone by comparing them with those acquired from the 3D photonic scanning method (3DPS). 17 male participants with diverse somatotypes were recruited. Each participant was scanned twice on the same day by a 3D whole-body scanner and photographed twice for the E-Zone analysis. The body volume data acquired from 3DPS was regarded as the reference against which the accuracy of the E-Zone was assessed. The relative technical error of measurement (TEM) of total body volume estimations was around 3% for E-Zone. E-Zone can estimate the segmental volumes of upper torso, lower torso, thigh, shank, upper arm and lower arm accurately (relative TEM<10%) but the accuracy for small segments including the neck, hand and foot were poor. In summary, E-Zone provides a reliable, inexpensive, portable, and simple method to obtain reasonable estimates of total body volume and to indicate segmental volume distribution.
Objective: Body volumes (BV) are used for calculating body composition to perform obesity assessments. Conventional BV estimation techniques, such as underwater weighing, can be difficult to apply. Advanced machine learning techniques enable multiple obesity-related body measurements to be obtained using a single-camera image; however, the accuracy of BV calculated using these techniques is unknown. This study aims to adapt and evaluate a machine learning technique, synthetic training for real accurate pose and shape (STRAPS), to estimate BV. Methods:The machine learning technique, STRAPS, was applied to generate threedimensional (3D) models from simulated two-dimensional (2D) images; these 3D models were then scaled with body stature and BV were estimated using regression models corrected for body mass. A commercial 3D scan dataset with a wide range of participants (n = 4318) was used to compare reference and estimated BV data. Results:The developed methods estimated BV with small relative standard errors of estimation (<7%) although performance varied when applied to different groups.The BV estimated for people with body mass index (BMI) < 30 kg/m 2 (1.9% for males and 1.8% for females) were more accurate than for people with BMI ≥ 30 kg/m 2 (6.9% for males and 2.4% for females). Conclusions:The developed method can be used for females and males with BMI < 30 kg/m 2 in BV estimation and could be used for obesity assessments at home or clinic settings.
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