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Gait analysis is the most typical use for 3D sports motion capture. To provide a more thorough knowledge of athlete movements, motion capture software-recorded movements are frequently combined with additional quantitative metrics, such as kinetic data from force plates.
In addition to being employed in the entertainment and video game sectors, 3D motion capture is also becoming more common in academic and medical settings. Motion capture is chosen over traditional computer animation in the film and entertainment industries because it can capture movements quickly and with little latency.
The Global Sports Motion Capture System market accounted for $XX Billion in 2022 and is anticipated to reach $XX Billion by 2030, registering a CAGR of XX% from 2023 to 2030.
In recent decades, significant developments have been made in the study of human movement in the contexts of sports biomechanics and rehabilitation.
The issue still remains in creating a motion analysis system that gathers reliable kinematic data swiftly, unobtrusively, and with external validity.
The motion analysis methods that are now most popular in sports biomechanics and rehabilitation do not allow for the automatic collection of kinematic data without the use of markers, controlled settings, and/or lengthy processing durations.
Since automatic markerless systems are clearly desired, it can be difficult to use motion capture on a regular basis in typical training or rehabilitation settings.
In vision-based motion analysis, movement is described by extracting data from a sequence of photographs.
Since then, motion analysis has developed significantly in tandem with significant technology improvements and the rising desire for quicker, more advanced methods to capture movement in a variety of contexts, from clinical gait analysis to video game animation.
In order to emphasise the shortcomings of current systems, this study will briefly discuss the history of the development and application of motion analysis techniques in sports and biomechanics.
Innovative markerless approaches created primarily for amusement provide a potentially effective answer, with some devices being able to measure sagittal plane angles during walking stride to within 2°–3°.
But different contexts call for different levels of accuracy, and the reliability of markerless systems hasn’t been properly tested for a variety of actions in a variety of settings.
To address the specific practical and accuracy requirements of motion analysis for sports and rehabilitation applications, further cooperative effort between computer vision experts and biomechanists is necessary to create such systems.
