Abstract
This paper introduces a novel framework for non-invasive gait analysis using a marker-based motion capture system and gait kinematic data. The system captures the 3D position of reflective markers on the human body, reconstructs the skeletal structure using inverse kinematics and probabilistic methods, detects gait events using displacement algorithms, and computes gait kinematic parameters. These parameters are used to identify and classify gait phases and patterns, and to diagnose and evaluate various injuries that impair human locomotion, such as traumatic brain injury, stroke, cerebral palsy, lower limb amputation, and spinal cord injury. The paper also explores the possibility of applying machine learning techniques for automated injury diagnosis using gait kinematic data, which would improve the diagnosis and treatment of patients with locomotor impairments, as well as foster research and innovation in biomechanics and clinical practice. The paper demonstrates the potential of this framework to revolutionize gait analysis in Vietnam, where it can enhance the quality of life and health outcomes for millions of people with locomotor impairments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cappozzo A (2018) Observing and revealing the hidden structure of the human form in motion throughout the centuries. In: Handbook of human motion. Springer, Cham, pp 3–15
Baker R, Esquenazi A, Benedetti MG, Desloovere K (2015) Gait analysis: clinical facts. Eur J Phys Rehabil Med 52:560–574
Hartley RI, Sturm P (1995) Triangulation. In: Hlaváč V, Šára R (eds) Computer analysis of images and patterns. Springer, Berlin and Heidelberg, pp 190–197
Anderson NI (1976) Retro-reflective materials
Hartley R, Zisserman A (2004) Multiple view geometry in computer vision, 2nd edn. Cambridge University Press, Cambridge
Triggs B, McLauchlan PF, Hartley RI, Fitzgibbon AW (2000) Bundle adjustment—a modern synthesis. In: Triggs B, Zisserman A, Szeliski R (eds) Vision algorithms: theory and practice. Springer, Berlin and Heidelberg, pp 298–372
Vicon DataStream SDK Documentation
Leboeuf F, Baker R, Barré A, Reay J, Jones R, Sangeux M (2019) The conventional gait model, an open-source implementation that reproduce the past but prepares for the future. Gait Posture 69:126. https://doi.org/10.1016/j.gaitpost.2019.01.034
Leboeuf F, Reay J, Jones R, Sangeux M (2019) The effect on conventional gait model kinematics and kinetics of hip joint centre equations in adult healthy gait. J Biomech 87:167–171. https://doi.org/10.1016/j.jbiomech.2019.02.010
Baker R (2011) Globographic visualisation of three dimensional joint angles. J Biomech 44:1885–1891. https://doi.org/10.1016/j.jbiomech.2011.04.031
Selbie WS, Brown MJ (2018) 3D dynamic pose estimation from marker-based optical data. In: Handbook of human motion. Springer, Cham, pp 81–100
Kepple TM, De Asha AR (2018) 3D dynamic probabilistic pose estimation from data collected using cameras and reflective markers. In: Handbook of human motion. Springer, Cham, pp 179–195
Zeni JA, Richards JG, Higginson JS (2008) Two simple methods for determining gait events during treadmill and overground walking using kinematic data. Gait Posture 27:710–714. https://doi.org/10.1016/j.gaitpost.2007.07.007
Sheffler LR, Chae J (2015) Hemiparetic gait. Phys Med Rehabil Clin N Am 26:611–623. https://doi.org/10.1016/j.pmr.2015.06.006
Awad LN, Palmer JA, Pohlig RT, Binder-Macleod SA, Reisman DS (2015) Walking speed and step length asymmetry modify the energy cost of walking after stroke. Neurorehabil Neural Repair 29:416–423. https://doi.org/10.1177/1545968314552528
Nguyen KHV, Pham A-D, Minh TB, Phan TTT, Do XP (2023) Gesture recognition model with multi-tracking capture system for human-robot interaction. In: 2023 International Conference on System Science and Engineering (ICSSE). IEEE, Ho Chi Minh, Vietnam, pp 6–11
Hady MFA, Schwenker F (2013) Semi-supervised learning. In: Bianchini M, Maggini M, Jain LC (eds) Handbook on neural information processing. Springer, Berlin and Heidelberg, pp 215–239
Chen X, Liang C, Huang D, Real E, Wang K, Liu Y, Pham H, Dong X, Luong T, Hsieh C-J, Lu Y, Le QV (2023) Symbolic discovery of optimization algorithms
Acknowledgements
This research is funded by the Ministry of Education and Training (Vietnam) under grant number B2024-VGU-02.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Nguyen, K.H.V., Nguyen, Q.H., Do, X.P., Phan, T.T.T. (2024). Underlying Injury Detection Model with High-Speed Multi-Tracking Motion Capture System. In: Todor, D., Kumar, S., Choi, SB., Nguyen-Xuan, H., Nguyen, Q.H., Trung Bui, T. (eds) Proceedings of the International Conference on Sustainable Energy Technologies. ICSET 2023. Green Energy and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-97-1868-9_30
Download citation
DOI: https://doi.org/10.1007/978-981-97-1868-9_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-1867-2
Online ISBN: 978-981-97-1868-9
eBook Packages: EnergyEnergy (R0)