抄録
Pose estimation using deep learning (DL) is expected to solve traditional problems faced by sports biomechanics, including limitations resulting from the application of reflective markers. For sports biomechanists to correctly utilize these pose estimation techniques, there is a need to elucidate the estimation and learning procedures used in pose estimation as well as to consider how to utilize them. Therefore, we aimed to review recently published major pose estimation models and to examine the availability of pose estimation in sports biomechanics. We observed that the main models were developed for simultaneous estimation of multiple persons, but none of the these were designed to rigorously estimate center of joint position which is mainly required in sports biomechanics. Further, all training datasets for these models were digitized positions that appeared as the joint centers of people in “in-the-wild” videos; moreover, these workers were non-professionals termed as “crowd-workers”. Therefore, regardless of the model quality, the dataset accuracy may be a bottleneck that impedes the estimation accuracy required in sports biomechanics. All the metrics used to verify the accuracy involved verification of the average estimation results of multiple joint points across the entire frame or multiple frames. Therefore, even with a high overall estimation accuracy, the accuracy of the estimated positions of the individual joints may be low. Taken together, it is difficult to utilize and calculate kinematic variables based on joint positions obtained through pose estimation. However, the existing pose estimation may help sports biomechanists calculate the movement periodization and number. To expand the utility of pose estimation in sports biomechanics, sports biomechanists should be actively involved in the development of pose estimation models and datasets.
https://ror.org/01tx1rq25 
