Feasibility of a Hip Flexion Feedback System For Controlling Exercise Intensity and Tibia Axial Peak Accelerations During Treadmill Walking

The ability to meet high exercise intensities is limited by the increased risk of injury in some clinical populations. Previous studies have linked large tibia peak positive accelerations resulting from running to increased risk of developing lower-extremity injury. The purpose of this study is to determine the feasibility of using a hip flexion feedback system (HFFS) to meet and maintain different exercise intensities while maintaining low tibia axial accelerations. Ten healthy participants were tested on a HFFS test and an independent walking/running test to meet exercise intensities of 40% and 60% of heart rate reserve (HRR). During the HFFS test, the HFFS controlled in real time the exercise intensity by directing individuals to specific maximum hip flexion targets during walking and providing visual information that assists them in maintaining low tibia peak positive accelerations during the initial contact phase. Maximum hip flexion targets during walking are calculated based on real-time readings of the participant’s heart rate. During the independent test, exercise intensity was controlled independently by the participant using treadmill speed. Compared to the independent test, using the HFFS at 60% HRR resulted in similar heart-rate error but lower tibia peak positive accelerations. No differences were observed for the 40% HRR intensity. This paper describes a novel exercise approach that uses the individual’s heart rate to calculate maximal hip flexion targets that an individual should meet during treadmill walking. The HFFS also provides tibia peak positive peak acceleration cues. Therefore, the HFFS can increase and control exercise intensities while maintaining low tibia accelerations. In particular, the HFFS might be an alternative strategy to meet moderate to vigorous exercise intensities in populations at risk of developing lower-extremity injuries

Feasibility of a Hip Flexion Feedback System For Controlling Exercise Intensity and Tibia Axial Peak Accelerations During Treadmill Walking

The ability to meet high exercise intensities is limited by the increased risk of injury in some clinical populations. Previous studies have linked large tibia peak positive accelerations resulting from running to increased risk of developing lower-extremity injury. The purpose of this study is to determine the feasibility of using a hip flexion feedback system (HFFS) to meet and maintain different exercise intensities while maintaining low tibia axial accelerations. Ten healthy participants were tested on a HFFS test and an independent walking/running test to meet exercise intensities of 40% and 60% of heart rate reserve (HRR). During the HFFS test, the HFFS controlled in real time the exercise intensity by directing individuals to specific maximum hip flexion targets during walking and providing visual information that assists them in maintaining low tibia peak positive accelerations during the initial contact phase. Maximum hip flexion targets during walking are calculated based on real-time readings of the participant’s heart rate. During the independent test, exercise intensity was controlled independently by the participant using treadmill speed. Compared to the independent test, using the HFFS at 60% HRR resulted in similar heart-rate error but lower tibia peak positive accelerations. No differences were observed for the 40% HRR intensity. This paper describes a novel exercise approach that uses the individual’s heart rate to calculate maximal hip flexion targets that an individual should meet during treadmill walking. The HFFS also provides tibia peak positive peak acceleration cues. Therefore, the HFFS can increase and control exercise intensities while maintaining low tibia accelerations. In particular, the HFFS might be an alternative strategy to meet moderate to vigorous exercise intensities in populations at risk of developing lower-extremity injuries

Quantifying obesity from anthropometric measures and body volume data

Obesity has become a serious problem in several developed and developing countries. Three-dimensional photonic scanning (3DPS) is a useful tool to obtain accurate anthropometric measures and body volume data for body shape quantification. Some traditional models have been developed to estimate body fat percentages from anthropometric measures or body volume data for body composition classification and obesity quantification. However, these traditional models are very sensitive to the errors in anthropometric measures and body volume data. Small errors in anthropometric measures or body volume data reduces accuracy of body fat percentages estimated from 3DPS and may lead to misclassifications when quantifying levels of obesity. In this study, pattern recognition techniques, neural networks, were applied to develop a new model which can classify obesity levels from a combination of anthropometric measures and body volume data without estimating body fat percentages. The developed model and the traditional models were applied to determine 2209 male participants’ body composition classes for obesity quantification. The accuracy of the new and the traditional models was determined by comparing the estimated body composition classes with the real body composition classes obtained from dual energy X-ray absorptiometry scanning output. The results showed that the accuracy of the developed model was better than the traditional models. Therefore, the developed model provides more accurate results in body composition classification for obesity quantification

Kinematic Patterns Associated with the Vertical Force Produced during the Eggbeater Kick

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

Validation of Body Volume Acquisition by Using Elliptical Zone Method

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

Technologies to Aid Public Understanding in Running Performance

Measurement technologies and visualisation techniques are changing the way public audiences engage with televised coverage of sport. However, the adoption of measurement technologies for broadcast coverage of running—to engage audiences and improve public understanding of performance—has been limited. This might reflect measurement challenges of athletic competition environments; athlete-worn measurement devices can be impractical, and video-based analyses typically require well-defined input videos for analysis (e.g., calibration, etc.). Recently, single-camera and calibration-independent video processing has advanced practical analyses of running performance in sports environments. This paper presents (1) the application of a method to quantify temporal running parameters using broadcast footage of 100 m sprint and 1-mile endurance running, (2) the application of human posture detection to quantify spatial running parameters using hand-held action camera footage and (3) examples of co-developed data visualisations, aimed at improving public engagement and understanding of running performance

AUTOMATIC CALCULATION OF PERSONAL BODY SEGMENT PARAMETERS WITH A MICROSOFT KINECT DEVICE

The purpose of this study was to introduce an automatic method for calculating personal body segment parameters (BSPs). In this automatic method, a Microsoft Kinect device was used to capture depth frames for measuring joint locations. The open source software, MakeHuman, was used for generating 3D human models by referring using the joint location data captured from the depth frames. Segmental meshes were obtained from the generated 3D human models and personal BSPs could be calculated automatically. The tests showed that the developed method can complete all of the processes without manual digitizing, anatomical landmark detection and medical scanner operation. Further research should be conducted to establish the accuracy of the segmental masses, centres of mass and moments of inertia acquired from the developed methods

Incorporating machine reliability issue and backlogging into the EMQ model - Part II: Random breakdown occurring in inventory piling time

This paper presents the second part of a research which is concerned with incorporating machine reliability issues and backlogging into the economic manufacturing quantity (EMQ) model. It may be noted that in a production system when back-ordering is permitted, a random machine failure can take place in either backorder filling stage or in on-hand inventory piling time. The first part of the research investigates the effect of a machine failure occurring in backorder filling stage on the optimal lot-size; while this paper (the second part of the research) studies the effect of random breakdown happening in inventory piling time on the optimal batch size for such an imperfect EMQ model. The objective is to determine the optimal replenishment lot-size that minimizes the overall productioninventory costs. Mathematical modelling is used and the renewal reward theorem is employed to cope with the variable cycle length. Hessian matrix equations are utilized to prove convexity of the cost function. Then, the optimal lot size for such a real-life imperfect manufacturing system is derived. Practitioners and managers in the field can adopt these replenishment policies to establish their own robust production plan accordingly

Automated body volume acquisitions from 3D structured-light scanning

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

Comparison of depth cameras for three-dimensional Reconstruction in Medicine

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