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Comparison of three methods for reconstructing 3D motion from 2D video recordings for low cost gait analysis systems

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: The aim of this study was to quantify the accuracy of 3D trajectory reconstructions performed from two planar video recordings, using three different reconstruction methods. Additionally, the recordings were carried out using easily available equipment, like built-in cellphone cameras, making the methods suitable for low-cost applications. Methods: A setup for 3D motion tracking was constructed and used to acquire 2D video recordings subsequently used to reconstruct the 3D trajectories by 1) merging appropriate coordinates, 2) merging coordinates with proportional scaling, and 3) calculating the 3D position based on markers’ projections on the viewing plane. As experimental verification, two markers moving at a fixed distance of 98.9 cm were used to assess the consistency of results. Next, gait analysis in five volunteers was carried out to quantify the differences resulting from different reconstruction methods. Results: Quantitative evaluation of the investigated 3D trajectories reconstruction methods showed significant differences between those methods, with the worst reconstruction approach resulting in a maximum error of 50% (standard deviation 13%), while the best resulting in a maximum error of 1% (standard deviation 0.44%). The gait analysis results showed differences in mean angles obtained with each reconstruction method reaching only 2°, which can be attributed to the limited measurement volume. Conclusions: Reconstructing 3D trajectory from 2D views without accounting for the “perspective error” results in significant reconstruction errors. The third method described in this study enables a significant reduction of this issue. Combined with the proposed setup, it provides a functional, low-cost gait analysis system.
Słowa kluczowe
Rocznik
Strony
271--277
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
autor
  • Institute of Metrology and Biomedical Engineering, Warsaw University of Technology, Poland
  • Modulo Studio Treningu EMS, Poland
Bibliografia
  • 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/10.1080/17461391.2018.1463397
  • 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.2020.07.002
  • 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_19
  • 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-2021-0026
  • 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.2020.2432
  • 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_7
  • 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-y
  • 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/s18092827
  • 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.2021.103275
  • 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.2020.09.017
  • 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/10.1080/03091902.2020.1822940
  • 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.2017.4410
  • 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/10.1117/12.2199378
  • 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.2007.02.001
  • 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.2015.4.1.001
  • 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.
  • 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.
  • 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-2016-0014
  • 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.2000.0897
  • 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.29.1.112
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c67a85f6-6f2e-44bb-81a4-6bc2c5747e35
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