Artificial Intelligence-Based Motion Capture: Current Technologies, Applications and Challenges

Author:

Baldinger Melanie,Lippmann Kevin,Senner Veit

Publisher

Springer Nature Switzerland

Reference51 articles.

1. Adesida, Y., Papi, E., & McGregor, A. H. (2019). Exploring the role of wearable technology in sport kinematics and kinetics: A systematic review. Sensors (Basel, Switzerland), 19(7). https://doi.org/10.3390/s19071597

2. Andriluka, M., Pishchulin, L., Gehler, P., & Schiele, B. (2014). 2D human pose estimation: New benchmark and state of the art analysis. In CVPR 2014: 2014 IEEE Conference on Computer Vision and Pattern Recognition : proceedings : 23–28 June 2014, Columbus, Ohio (pp. 3686–3693). IEEE Computer Society. https://doi.org/10.1109/CVPR.2014.471

3. Badiola-Bengoa, A., & Mendez-Zorrilla, A. (2021). A systematic review of the application of camera-based human pose estimation in the field of sport and physical exercise. Sensors (Basel, Switzerland), 21(18). https://doi.org/10.3390/s21185996

4. Baldinger, M., & Senner, V. (2022). Openpose and its current applications in sports and exercise science: A review. In D. Krumm, S. Schwanitz, & S. Odenwald (Eds.), Spinfortec2022: Tagungsband zum 14. Symposium der Sektion Sportinformatik und Sporttechnologie der Deutschen Vereinigung für Sportwissenschaft (dvs), Chemnitz 29. - 30. September 2022 (pp. 14–17). Universitätsverlag Chemnitz. https://nbn-resolving.org/urn:nbn:de:bsz:ch1-qucosa2-807512

5. Bertram, J., Krüger, T., Röhling, H. M., Jelusic, A., Mansow-Model, S., Schniepp, R., Wuehr, M., & Otte, K. (2023). Accuracy and repeatability of the Microsoft Azure Kinect for clinical measurement of motor function. PLoS ONE, 18(1), e0279697. https://doi.org/10.1371/journal.pone.0279697

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