[
1. van der Kruk E, Reijne MM. Accuracy of human motion capture systems for sport applications; state-of-the-art review. Eur J Sport Sci. 2018;18(6):806-819. https://doi.org/10.1080/17461391.2018.146339710.1080/17461391.2018.146339729741985
]Search in Google Scholar
[
2. Syczewska M, Kocel K, Swiecicka A, et al. Selection of gait parameters for modified Gillette Gait Index using Hellwig Correlation Based Filter method, random forest method, and correlation methods. Biocybern Biomed Eng. 2020;40(3):1267-1276. https://doi.org/10.1016/J.BBE.2020.07.00210.1016/j.bbe.2020.07.002
]Search in Google Scholar
[
3. Łysoń-Uklańska B, Ścibek J, Bienias K, Wit A. Analysis of Ground Reaction Forces and Kinematic Response to Gait Perturbation During Mid- to Terminal Stance Phase of the Gait Cycle. Adv Intell Syst Comput. 2020;1223:165-173. https://doi.org/10.1007/978-3-030-52180-6_1910.1007/978-3-030-52180-6_19
]Search in Google Scholar
[
4. Łysoń-Uklańska B, Błazkiewicz M, Kwacz M, Wit A. Muscle Force Patterns in Lower Extremity Muscles for Elite Discus Throwers, Javelin Throwers and Shot-Putters - A Case Study. J Hum Kinet. 2021;78(1):5-14. https://doi.org/10.2478/hukin-2021-002610.2478/hukin-2021-0026812096034025859
]Search in Google Scholar
[
5. Groote F De, Falisse A. Perspective on musculoskeletal modelling and predictive simulations of human movement to assess the neuromechanics of gait. Proc R Soc B. 2021;288(1946). https://doi.org/10.1098/RSPB.2020.243210.1098/rspb.2020.2432793508233653141
]Search in Google Scholar
[
6. Tecante K, Seehaus F, Welke B, et al. Clinical gait analysis and musculoskeletal modeling. In: Magnenat-Thalmann N, Ratib O, Choi H. (eds) 3D Multiscale Physiological Human. Springer, London. 2014. https://doi.org/10.1007/978-1-4471-6275-9_710.1007/978-1-4471-6275-9_7
]Search in Google Scholar
[
7. Colyer SL, Evans M, Cosker DP, Salo AIT. A Review of the Evolution of Vision-Based Motion Analysis and the Integration of Advanced Computer Vision Methods Towards Developing a Markerless System. Sport Med - Open. 2018;4(1):1-15. https://doi.org/10.1186/s40798-018-0139-y10.1186/s40798-018-0139-y598669229869300
]Search in Google Scholar
[
8. Liberadzki P, Adamczyk M, Witkowski M, Sitnik R. Structured-light-based system for shape measurement of the human body in motion. Sensors (Switzerland). 2018;18(9). https://doi.org/10.3390/s1809282710.3390/s18092827616504930150558
]Search in Google Scholar
[
9. Desmarais Y, Mottet D, Slangen P, Montesinos P. A review of 3D human pose estimation algorithms for markerless motion capture. Comput Vis Image Underst. 2021;212:103275. https://doi.org/10.1016/J.CVIU.2021.10327510.1016/j.cviu.2021.103275
]Search in Google Scholar
[
10. Mathis A, Schneider S, Lauer J, Mathis MW. A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives. Neuron. 2020;108(1):44-65. https://doi.org/10.1016/J.NEURON.2020.09.01710.1016/j.neuron.2020.09.01733058765
]Search in Google Scholar
[
11. Khera P, Kumar N. Role of machine learning in gait analysis: a review. Journal of Medical Engineering & Technology. 2020;44(8):441-467. https://doi.org/10.1080/03091902.2020.182294010.1080/03091902.2020.182294033078988
]Search in Google Scholar
[
12. Magdin M. Simple MoCap System for Home Usage. Int J Interact Multimed Artif Intell. 2017;4(4):80. https://doi.org/10.9781/ijimai.2017.441010.9781/ijimai.2017.4410
]Search in Google Scholar
[
13. Rajkiewicz P, Łepkowska K, Cygan S. Video markers tracking methods for bike fitting. In: Romaniuk RS, ed. Proceedings of SPIE -The International Society for Optical Engineering. Vol 9662. International Society for Optics and Photonics; 2015:96621G. https://doi.org/10.1117/12.219937810.1117/12.2199378
]Search in Google Scholar
[
14. Kolahi A, Hoviattalab M, Rezaeian T, Alizadeh M, Bostan M, Mokhtarzadeh H. Design of a marker-based human motion tracking system. Biomed Signal Process Control. 2007;2(1):59-67. https://doi.org/10.1016/J.BSPC.2007.02.00110.1016/j.bspc.2007.02.001
]Search in Google Scholar
[
15. Chantara W, Mun J-H, Shin D-W, Ho Y-S. Object Tracking using Adaptive Template Matching. IEIE Trans Smart Process Comput. 2015;4(1):1-9. https://doi.org/10.5573/ieiespc.2015.4.1.00110.5573/IEIESPC.2015.4.1.001
]Search in Google Scholar
[
16. Cygan S, Kaluzynski K, Lesniak B. Displacement estimation methods for elastography - a phantom study. Eng Mech. 2005;12(5):361-368. http://www.engineeringmechanics.cz/pdf/12_5_361.a.pdf.
]Search in Google Scholar
[
17. Shortest Distance Between Two Lines in N dimensions - File Exchange - MATLAB Central. https://www.mathworks.com/matlabcentral/fileexchange/29130-shortest-distance-between-two-lines-in-n-dimensions. Accessed March 21, 2020.
]Search in Google Scholar
[
18. Clark CCT, Barnes CM, Holton M, Summers HD, Stratton G. A Kinematic Analysis of Fundamental Movement Skills. Sport Sci Rev. 2016;25(3-4):261-275. https://doi.org/10.1515/ssr-2016-001410.1515/ssr-2016-0014
]Search in Google Scholar
[
19. Moeslund TB, Granum E. A Survey of Computer Vision-Based Human Motion Capture. Comput Vis Image Underst. 2001;81(3):231-268. https://doi.org/10.1006/CVIU.2000.089710.1006/cviu.2000.0897
]Search in Google Scholar
[
20. Thewlis D, Bishop C, Daniell N, Paul G. Next-generation low-cost motion capture systems can provide comparable spatial accuracy to high-end systems. J Appl Biomech. 2013;29(1):112-117. https://doi.org/10.1123/jab.29.1.11210.1123/jab.29.1.11222813783
]Search in Google Scholar
