A markerless motion capture technique for sport performance analysis and injury prevention: Toward a ‘big data’, machine learning future

2015 ◽  
Vol 19 ◽  
pp. e79 ◽  
Author(s):  
J. Alderson
2020 ◽  
Author(s):  
Premalatha Jayapaul ◽  
Aswini Balasundaram ◽  
Kavi Priya Dharshini Seturamalingam ◽  
Kavithra Sekar

2019 ◽  
Vol 68 (11) ◽  
pp. 4456-4471 ◽  
Author(s):  
Simone Pasinetti ◽  
M. Muneeb Hassan ◽  
Jorg Eberhardt ◽  
Matteo Lancini ◽  
Franco Docchio ◽  
...  

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Rabiu Muazu Musa ◽  
Anwar P. P. Abdul Majeed ◽  
Muhammad Zuhaili Suhaimi ◽  
Mohd Azraai Mohd Razman ◽  
Mohamad Razali Abdullah ◽  
...  

2019 ◽  
Author(s):  
Nobuyasu Nakano ◽  
Tetsuro Sakura ◽  
Kazuhiro Ueda ◽  
Leon Omura ◽  
Arata Kimura ◽  
...  

AbstractThere is a need within human movement sciences for a markerless motion capture system, which is easy to use and suffciently accurate to evaluate motor performance. This study aims to develop a 3D markerless motion capture technique, using OpenPose with multiple synchronized video cameras, and examine its accuracy in comparison with optical marker-based motion capture. Participants performed three motor tasks (walking, countermovement jumping, and ball throwing), with these movements measured using both marker-based optical motion capture and OpenPose-based markerless motion capture. The differences in corresponding joint positions, estimated from the two different methods throughout the analysis, were presented as a mean absolute error (MAE). The results demonstrated that, qualitatively, 3D pose estimation using markerless motion capture could correctly reproduce the movements of participants. Quantitatively, of all the mean absolute errors calculated, approximately 47% were less than 20 mm and 80% were less than 30 mm. However, 10% were greater than 40 mm. The primary reason for mean absolute errors exceeding 40mm was that OpenPose failed to track the participant’s pose in 2D images owing to failures, such as recognition of an object as a human body segment, or replacing one segment with another depending on the image of each frame. In conclusion, this study demonstrates that, if an algorithm that corrects all apparently wrong tracking can be incorporated into the system, OpenPose-based markerless motion capture can be used for human movement science with an accuracy of 30mm or less.


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