BackgroundThe 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.Main bodyThis 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.ConclusionsThis 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.

A novel approach of analyzing complete ground reaction force waveforms rather than discrete kinetic variables can provide new insight to sprint biomechanics. This study aimed to understand how these waveforms are associated with better performance across entire sprint accelerations. Twenty-eight male track and field athletes (100-m personal best times: 10.88 to 11.96 seconds) volunteered to participate. Ground reaction forces produced across 24 steps were captured during repeated (two to five) maximal-effort sprints utilizing a 54-force-plate system. Force data (antero-posterior, vertical, resultant, and ratio of forces) across each contact were registered to 100% of stance and averaged for each athlete. Statistical parametric mapping (linear regression) revealed specific phases of stance where force was associated with average horizontal external power produced during that contact. Initially, antero-posterior force production during mid-late propulsion (eg, 58%-92% of stance for the second ground contact) was positively associated with average horizontal external power. As athletes progressed through acceleration, this positive association with performance shifted toward the earlier phases of contact (eg, 55%-80% of stance for the eighth and 19%-64% for the 19th ground contact). Consequently, as athletes approached maximum velocity, better athletes were more capable of attenuating the braking forces, especially in the latter parts of the eccentric phase. These unique findings demonstrate a shift in the performance determinants of acceleration from higher concentric propulsion to lower eccentric braking forces as velocity increases. This highlights the broad kinetic requirements of sprinting and the conceivable need for athletes to target improvements in different phases separately with demand-specific exercises.

Forces applied to the ground during sprinting are vital to performance. This study aimed to understand how specific aspects of ground reaction force waveforms allow some individuals to continue to accelerate beyond the velocity plateau of others. Twenty-eight male sprint specialists and 24 male soccer players performed maximal-effort 60-m sprints. A 54-force-plate system captured ground reaction forces, which were used to calculate horizontal velocity profiles. Touchdown velocities of steps were matched (8.00, 8.25, and 8.50 m/s), and the subsequent ground contact forces were analyzed. Mean forces were compared across groups and statistical parametric mapping (t tests) assessed for differences between entire force waveforms. When individuals contacted the ground with matched horizontal velocity, ground contact durations were similar. Despite this, sprinters produced higher average horizontal power (15.7-17.9 W/kg) than the soccer players (7.9-11.9 W/kg). Force waveforms did not differ in the initial braking phase (0%-~20% of stance). However, sprinters attenuated eccentric force more in the late braking phase and produced a higher antero-posterior component of force across the majority of the propulsive phase, for example, from 31%-82% and 92%-100% of stance at 8.5 m/s. At this velocity, resultant forces were also higher (33%-83% and 86%-100% of stance) and the force vector was more horizontally orientated (30%-60% and 95%-98% of stance) in the sprinters. These findings illustrate the mechanisms which allowed the sprinters to continue accelerating beyond the soccer players' velocity plateau. Moreover, these force production demands provide new insight regarding athletes' strength and technique training requirements to improve acceleration at high velocity.

Human movement researchers are often restricted to laboratory environments and data capture techniques that are time and/or resource intensive. Markerless pose estimation algorithms show great potential to facilitate large scale movement studies ‘in the wild’, i.e., outside of the constraints imposed by marker-based motion capture. However, the accuracy of such algorithms has not yet been fully evaluated. We computed 3D joint centre locations using several pre-trained deep-learning based pose estimation methods (OpenPose, AlphaPose, DeepLabCut) and compared to marker-based motion capture. Participants performed walking, running and jumping activities while marker-based motion capture data and multi-camera high speed images (200 Hz) were captured. The pose estimation algorithms were applied to 2D image data and 3D joint centre locations were reconstructed. Pose estimation derived joint centres demonstrated systematic differences at the hip and knee (~ 30–50 mm), most likely due to mislabeling of ground truth data in the training datasets. Where systematic differences were lower, e.g., the ankle, differences of 1–15 mm were observed depending on the activity. Markerless motion capture represents a highly promising emerging technology that could free movement scientists from laboratory environments but 3D joint centre locations are not yet consistently comparable to marker-based motion capture.

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.

Ground reaction forces produced on the blocks determine an athlete’s centre of mass motion during the sprint start, which is crucial to sprint performance. This study aimed to understand how force waveforms are associated with better sprint start performance. Fifty-seven sprinters (from junior to world elite) performed a series of block starts during which the ground reaction forces produced by the legs and arms were separately measured. Statistical parametric mapping (linear regression) revealed specific phases of these waveforms where forces were associated with average horizontal external power. Better performances were achieved by producing higher forces and directing the force vector more horizontally during the initial parts of the block phase (17–34% and 5–37%, respectively). During the mid-push (around the time of rear block exit: ∼54% of the block push), magnitudes of front block force differentiated performers, but orientation did not. Consequently, the ability to sustain high forces during the transition from bilateral to unilateral pushing was a performance-differentiating factor. Better athletes also exhibited a higher ratio of forces on the front block in the latter parts of unilateral pushing (81–92% of the block push), which seemed to allow these athletes to exit the blocks with lower centre of mass projection angles. Training should reflect these kinetic requirements, but also include technique-based aspects to increase both force production and orientation capacities. Specific training focused on enhancing anteroposterior force production during the transition between double- to single-leg propulsion could be beneficial for overall sprint start performance.

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.

Purpose: Sprint kinematics have been linked to hamstring injury and performance. This study aimed to examine if a specific 6-week multimodal intervention, combining lumbopelvic control and unning technique exercises, induced changes in pelvis and lower-limb kinematics at maximal speed and improved sprint performance. Methods: Healthy amateur athletes were assigned to a control or intervention group (IG). A sprint test with 3-dimensional kinematic measurements was performed before (PRE) and after (POST) 6 weeks of training. The IG program included 3 weekly sessions integrating coaching, strength and conditioning, and physical therapy approaches (eg, manual therapy, mobility, lumbopelvic control, strength and sprint “front-side mechanics”-oriented drills). Results: Analyses of variance showed no between-group differences at PRE. At POST, intragroup analyses showed PRE–POST differences for the pelvic (sagittal and frontal planes) and thigh kinematics and improved sprint performance (split times) for the IG only. Specifically, IG showed (1) a lower anterior pelvic tilt during the late swing phase, (2) greater pelvic obliquity on the free-leg side during the early swing phase, (3) higher vertical position of the front-leg knee, (4) an increase in thigh angular velocity and thigh retraction velocity, (5) lower between-knees distance at initial contact, and (6) a shorter ground contact duration. The intergroup analysis revealed disparate effects (possibly to very likely) in the most relevant variables investigated. Conclusion: The 6-week multimodal training program induced clear pelvic and lower-limb kinematic changes during maximal speed sprinting. These alterations may collectively be associated with reduced risk of muscle strain and were concomitant with significant sprint performance improvement.