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.

Background Markerless motion capture has the potential to perform movement analysis with reduced data collection and processing time compared to marker-based methods. This technology is now starting to be applied for clinical and rehabilitation applications and therefore it is crucial that users of these systems understand both their potential and limitations. This literature review aims to provide a comprehensive overview of the current state of markerless motion capture for both single camera and multi-camera systems. Additionally, this review explores how practical applications of markerless technology are being used in clinical and rehabilitation settings, and examines the future challenges and directions markerless research must explore to facilitate full integration of this technology within clinical biomechanics. Methodology A scoping review is needed to examine this emerging broad body of literature and determine where gaps in knowledge exist, this is key to developing motion capture methods that are cost effective and practically relevant to clinicians, coaches and researchers around the world. Literature searches were performed to examine studies that report accuracy of markerless motion capture methods, explore current practical applications of markerless motion capture methods in clinical biomechanics and identify gaps in our knowledge that are relevant to future developments in this area. Results Markerless methods increase motion capture data versatility, enabling datasets to be re-analyzed using updated pose estimation algorithms and may even provide clinicians with the capability to collect data while patients are wearing normal clothing. While markerless temporospatial measures generally appear to be equivalent to marker-based motion capture, joint center locations and joint angles are not yet sufficiently accurate for clinical applications. Pose estimation algorithms are approaching similar error rates of marker-based motion capture, however, without comparison to a gold standard, such as bi-planar videoradiography, the true accuracy of markerless systems remains unknown. Conclusions Current open-source pose estimation algorithms were never designed for biomechanical applications, therefore, datasets on which they have been trained are inconsistently and inaccurately labelled. Improvements to labelling of open-source training data, as well as assessment of markerless accuracy against gold standard methods will be vital next steps in the development of this technology.

Laboratory methods that are required to calculate highly precise jump heights during experimental research have never been sufficiently compared and examined. Our first aim was to compare jumping outcome measures of the same jump, using four different methods (double integration from force plate data, rigid‐body modeling from motion capture data, marker‐based video tracking, and a hybrid method), separately for countermovement and squat jumps. Additionally, laboratory methods are often unsuitable for field use due to equipment or time restrictions. Therefore, our second aim was to improve an additional field‐based method (flight‐time method), by combining this method with an anthropometrically scaled constant. Motion capture and ground reaction forces were used to calculate jump height of twenty‐four participants who performed five maximal countermovement jumps and five maximal squat jumps. Within‐participant mean and standard deviation of jump height, flight distance, heel‐lift, and take‐off velocity were compared for each of the four methods. All four methods calculated countermovement jump height with low variability and are suitable for research applications. The double integration method had significant errors in squat jump height due to integration drift, and all other methods had low variability and are therefore suitable for research applications. Rigid‐body modeling was unable to determine the position of the center of mass at take‐off in both jumping movements and should not be used to calculate heel‐lift or flight distance. The flight‐time method was greatly improved with the addition of an anthropometrically scaled heel‐lift constant, enabling this method to estimate jump height and subsequently estimate power output in the field.

The preferred movement strategies that humans choose to produce work for movement are not fully understood. Previous studies have demonstrated an important contribution of elastic energy stored within the Achilles tendon (AT) during jumping. This study aimed to alter energy available for storage in the AT to examine changes in how jumpers distribute work among lower limb joints. Participants (n = 16) performed maximal and sub-maximal jumps under two paradigms, matched for increasing total work output by manipulating jump height or adding body mass. Motion capture and ground reaction force data were combined in an inverse dynamics analysis to compute ankle, knee and hip joint kinetics. Results demonstrated higher peak moments about the ankle joint with added body mass (+26 Nm), likely resulting in additional energy storage in the AT. Work at the ankle joint increased proportionally with added mass, maintaining a constant contribution (~64%) to total work that was not matched with increasing jump height (−14%). This implies greater energy storage and return by the AT with added mass but not with increased height. When total work during jumping is constant but energy stored in tendons is not, humans prioritise the use of stored elastic energy over muscle work.

Muscle contractile mechanics induced by the changing demands of human movement have the potential to influence our movement strategies. This study examined fascicle length changes of the triceps surae during jumping with added mass or increasing jump height to determine whether the chosen movement strategies were associated with relevant changes in muscle contractile properties. Sixteen participants jumped at sub-maximal and maximal intensities while total net work was matched via two distinct paradigms: (1) adding mass to the participant or (2) increasing jump height. Electromyography (EMG) and ultrasound analyses were performed to examine muscle activation, fascicle length and fascicle velocity changes of the triceps surae during jumping. Integrated EMG was significantly higher in the added mass paradigm with no difference in mean or maximal EMG, indicating that the muscle was activated for a significantly longer period of time but not activated to a greater intensity. Fascicle shortening velocity was slower with added mass compared than with increasing jump height; therefore, intrinsic forcevelocity properties probably enabled increased force production. Improved fascicle contractile mechanics paired with a longer activation period probably produced a consistently larger fascicle force, enabling a greater impulse about the ankle joint. This may explain why previous research found that participants used an anklecentred strategy for work production in the added mass paradigm and not in the jump height paradigm. The varied architecture of muscles within the lower limb may influence which muscles we choose to employ for work production under different task constraints.