The study of human movement within sports biomechanics and rehabilitation settings has made considerable progress over recent decades. However, developing a motion analysis system that collects accurate kinematic data in a timely, unobtrusive and externally valid manner remains an open challenge. This narrative review considers the evolution of methods for extracting kinematic information from images, observing how technology has progressed from laborious manual approaches to optoelectronic marker-based systems. The motion analysis systems which are currently most widely used in sports biomechanics and rehabilitation do not allow kinematic data to be collected automatically without the attachment of markers, controlled conditions and/or extensive processing times. These limitations can obstruct the routine use of motion capture in normal training or rehabilitation environments, and there is a clear desire for the development of automatic markerless systems. Such technology is emerging, often driven by the needs of the entertainment industry, and utilising many of the latest trends in computer vision and machine learning. However, the accuracy and practicality of these systems has yet to be fully scrutinised, meaning such markerless systems are not currently in widespread use within biomechanics. This review aims to introduce the key state-of-the-art in markerless motion capture research from computer vision that is likely to have a future impact in biomechanics, while considering the challenges with accuracy and robustness that are yet to be addressed.

The ability to accurately and non-invasively measure 3D mass centre positions and their derivatives can provide rich insight into the physical demands of sports training and competition. This study examines a method for non-invasively measuring mass centre velocities using markerless human pose estimation and Kalman smoothing. Marker (Qualysis) and markerless (OpenPose) motion capture data were captured synchronously for sprinting and skeleton push starts. Mass centre positions and velocities derived from raw markerless pose estimation data contained large errors for both sprinting and skeleton pushing (mean ± SD = 0.127 ± 0.943 and −0.197 ± 1.549 m·s⁻¹, respectively). Signal processing methods such as Kalman smoothing substantially reduced the mean error (±SD) in horizontal mass centre velocities (0.041 ± 0.257 m·s⁻¹) during sprinting but the precision remained poor. Applying pose estimation to activities which exhibit unusual body poses (e.g., skeleton pushing) appears to elicit more erroneous results due to poor performance of the pose estimation algorithm. Researchers and practitioners should apply these methods with caution to activities beyond sprinting as pose estimation algorithms may not generalise well to the activity of interest. Retraining the model using activity specific data to produce more specialised networks is therefore recommended.

Dual-energy x-ray absorptiometry (DXA) imaging is considered to provide a valid and reliable estimation of body composition when stringent scanning protocols are adopted. However, applied practitioners are not always able to achieve this level of control and the subsequent impact on measurement precision is not always taken into account when evaluating longitudinal body composition changes. The primary aim of this study was to establish the reliability of DXA in an applied elite sport setting to investigate whether real body composition changes can be detected. Additionally, the performance implications of these changes during the training year were investigated. Forty-eight well-trained athletes (from four diverse sports) underwent two DXA scans using a ‘real-world’ approach (with limited pre-scan controls), typically within 48 h, to quantify typical error of measurement (TEM). Twenty-five athletes underwent further scans, before and after specific training and competition blocks. ‘True’ body composition changes were evaluated using 2 × TEM thresholds. Twelve bob skeleton athletes also performed countermovement jump and leg press tests at each time point. Many ‘true’ body composition changes were detected and coincided with the primary training emphases (e.g. lean mass gains during hypertrophy-based training). Clear relationships ( r ± 90% CI) were observed between performance changes (countermovement jump and leg press) and changes in lean mass (0.53 ± 0.26 and 0.35 ± 0.28, respectively) and fat mass (−0.44 ± 0.27 and −0.37 ± 0.28, respectively). DXA was able to detect real body composition changes without the use of stringent scanning controls. Associations between changes in body composition and performance demonstrated the potential influence of these changes on strength and power indices.

Purpose:: An extensive battery of physical tests is typically employed to evaluate athletic status and/or development, often resulting in a multitude of output variables. The authors aimed to identify independent physical predictors of elite skeleton start performance to overcome the general problem of practitioners employing multiple tests with little knowledge of their predictive utility.Methods:: Multiple 2-d testing sessions were undertaken by 13 high-level skeleton athletes across a 24-wk training season and consisted of flexibility, dry-land push-track, sprint, countermovement-jump, and leg-press tests. To reduce the large number of output variables to independent factors, principal-component analysis (PCA) was conducted. The variable most strongly correlated to each component was entered into a stepwise multiple-regression analysis, and K-fold validation assessed model stability.Results:: PCA revealed 3 components underlying the physical variables: sprint ability, lower-limb power, and strength–power characteristics. Three variables that represented these components (unresisted 15-m sprint time, 0-kg jump height, and leg-press force at peak power, respectively) significantly contributed (P < .01) to the prediction (R² = .86, 1.52% standard error of estimate) of start performance (15-m sled velocity). Finally, the K-fold validation revealed the model to be stable (predicted vs actual R² = .77; 1.97% standard error of estimate).Conclusions:: Only 3 physical-test scores were needed to obtain a valid and stable prediction of skeleton start ability. This method of isolating independent physical variables underlying performance could improve the validity and efficiency of athlete monitoring, potentially benefitting sport scientists, coaches, and athletes alike.