Three-dimensional (3D) pose of rigid segments can be fully specified by six degrees of freedom (DOF): three relating to translation and three defining orientation. To make this process more tractable, the structure of the human body is usually simplified as a series of rigid bodies connected by frictionless rotational joints. However, accurate quantification of whole-body pose can be a difficult problem to solve since the human body is an extremely complex, highly articulated, self-occluding and only partially rigid entity. Through the identification of common object features in successive images, displacement data can be “tracked” over time. Optical motion analysis requires the estimation of the position and orientation (pose) of an object across image sequences. General Principles and Requirements of Vision-Based Motion Analysis in Sports Biomechanics and Rehabilitation This review considers how these new technologies could revolutionise the fields of sports biomechanics and rehabilitation by broadening the applications of motion analysis to include everyday training or competition environments. The state-of-the-art technologies from computer vision and machine learning, which have started to emerge within the biomechanics community, are introduced. The aim of this review is to cover some of the history of the development and use of motion analysis methods within sports and biomechanics, highlighting the limitations of existing systems. īiomechanical tools have developed considerably from manual annotation of images to marker-based optical trackers, inertial sensor-based systems and markerless systems using sophisticated human body models, computer vision and machine learning algorithms. Within sports biomechanics and rehabilitation applications, quantitative analysis of human body kinematics is a powerful tool that has been used to understand the performance determining aspects of technique, identify injury risk factors, and facilitate recovery from injury or trauma. Motion analysis has since evolved substantially in parallel with major technological advancements and the increasing demand for faster, more sophisticated techniques to capture movement in a wide range of settings ranging from clinical gait assessment to video game animation. It can be traced back to the late nineteenth century and the pioneering work of Eadweard Muybridge who first developed techniques to capture image sequences of equine gait. Vision-based motion analysis involves extracting information from sequential images in order to describe movement. 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. 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. 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. 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. 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. 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. However, developing a motion analysis system that collects accurate kinematic data in a timely, unobtrusive and externally valid manner remains an open challenge. The study of human movement within sports biomechanics and rehabilitation settings has made considerable progress over recent decades.
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