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Computer – aided method for lower limbs kinematic analysis

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Warianty tytułu
PL
Komputerowa metoda analizy kinematyki kończyn dolnych
Języki publikacji
EN
Abstrakty
EN
The aim of this paper is to propose a novel method that enables kinematic analysis of motion capture (MoCap) data of lower limbs activities by comparison of body joints trajectories to the reference template. We propose an appropriate human body kinematic model, MoCap aligning procedure and heuristic evaluation with Dynamic Time Warping (DTW) - based approach. In contrast to other state-of-the-art papers, where analysis is performed on the single joint on the selected two-dimensional plane, we performed three-dimensional evaluation of human body by analyzing the whole kinematic chain jointly. This approach allows us to find which body joints affected the difference between the input and reference recordings the most. This is valuable information that a person who evaluates MoCap data expects to find. We have also performed kinematic analysis applying commonly used kinematic parameters proposed in state-of-the-art researches in order to show that in our case, when there is no restriction on speed or dynamic of action to be analyzed, those parameters cannot be used to draw valuable conclusions. We have tested our method on a dataset consisting recordings of four karate athletes with various experience in Shorin Ryu karate school. While comparing our algorithm’s results to experts evaluation the true positive rate equals 0.93 while negative rate 0.96.
PL
W pracy zaproponowano nową metodę analizy kinematyki kończyn dolnych przy pomocy nagrań motion capture (MoCap). Zaproponowane rozwiązanie pozwala na kompleksową analize całosci łańcucha kinematycznego. Przetestowaliśmy zaproponowany algorytm na zbiorze danych zawierającym nagrania czterech zawodników Shorin Ryu karate uzyskując zadawalające wyniki w porównaniu do analogicznej ewaluacji przeprowadzonej przez eksperta.
Rocznik
Strony
15--25
Opis fizyczny
Bibliogr. 31 poz., rys., tab.
Twórcy
  • Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie, aleja Adama Mickiewicza 30, 30-059 Kraków
  • Uniwersytet Pedagogiczny im. Komisji Edukacji Narodowej w Krakowie, ul. Podchorążych 2, 30-084 Kraków
Bibliografia
  • [1] Nagesh Yadav, Chris J. Bleakley, Olive Lennon, Wearable Absolute 6 DOF Exercise Training System for Post Stroke Rehabilitation. In: Byrne, D. et al (eds.). Proceedings of the Fourth Irish Human Computer Interaction Conference (iHCI 2010) 2-3 September 2010, Dublin, Ireland. Dublin City University, 2010- 09-02. Available at: http://hdl.handle.net/10197/3861
  • [2] T N Brown, R M Palmieri-Smith, S G McLean, Sex and limb differences in hip and knee kinematics and kinetics during anticipated and unanticipated jump landings: implications for anterior cruciate ligament injury, Br J Sports Med 2009;43:1049–1056. doi:10.1136/bjsm.2008.055954
  • [3] Oonagh M Giggins, Kevin T Sweeney, Brian Caulfield, Rehabilitation exercise assessment using inertial sensors: a crosssectional analytical study, Journal of NeuroEngineering and Rehabilitation201411:158, doi 10.1186/1743-0003-11-158
  • [4] E. Preuschl, M. Hassmann and A. Baca, A Kinematic Analysis of the Jumping Front-Leg Axe-Kick in Taekwondo, Journal of Sports Science and Medicine (2016) 15, 92-101
  • [5] David Webster, Ozkan Celik, Experimental Evaluation of Microsoft Kinect’s Accuracy and Capture Rate for Stroke Rehabilitation Applications, IEEE Haptics Symposium 2014, 23-26 February, Houston, Tx, USA, DOI: 10.1109/HAPTICS. 2014.6775498
  • [6] Xiaoming Zhang, Lin Xu, Yang Zhang, A Wireless Evaluation System for In-home Physical Rehabilitation, Control And Decision Conference (CCDC), 2017 29th Chinese, DOI: 10.1109/CCDC.2017.7979089
  • [7] Kurillo G, Koritnik T, Bajd T, Bajcsy R., Real-Time 3D Avatars for Tele-rehabilitation in Virtual Reality, Stud Health Technol Inform. 2011;163:290-6.
  • [8] Wei Lu, Wei Zong, Weiwei Xing, Ergude Bao, Gait recognition based on joint distribution of motion angles, Journal of Visual Languages and Computing, 25 (2014) 754–763
  • [9] Heike Brock, Yuji Ohgi, Kazuya Seo, Development of an automated motion evaluation system from wearable sensor devices for ski jumping, Procedia Engineering 147 (2016) 694 – 699
  • [10] Aras Yurtman, Billur Barshan, Automated evaluation of physical therapyexercises using multi-template dynamictime warping on wearable sensor signals, Computer Methods and Programs in Biomedicine, Volume 117, Issue 2, November 2014, Pages 189-207
  • [11] Yongdan Nie, Lianmei Liu, Hierarchical retrieval of human motion data based on semantic feature, The 11th International Conference on Computer Science & Education (ICCSE 2016), August 23-25, 2016. Nagoya University, Japan
  • [12] J.L. Raheja, M. Minhas, D. Prashanth, T. Shah, A. Chaudhary, Robust gesture recognition using Kinect: A comparison between DTWand HMM, Optik 126 (2015) 1098–1104, 24 PRZEGLA˛D ELEKTROTECHNICZNY, ISSN 0033-2097, R. 96 NR 6/2020
  • [13] GitHub repository with source code for this research (implementation in R language) https://github.com/ browarsoftware/LowerLimbsKinematicAnalysis.R
  • [14] Website with dataset used in this reserch http://www.gdl. org.pl
  • [15] Tomasz Hachaj, Marcin Piekarczyk, Marek R. Ogiela, Human Actions Analysis: Templates Generation, Matching and Visualization Applied to Motion Capture of Highly- Skilled Karate Athletes, Sensors 2017, 17(11), 2590; doi:10.3390/s17112590
  • [16] Tomasz Hachaj, Marek R. Ogiela, Multimed Tools Appl (2018). https://doi.org/10.1007/s11042-018-6137-8
  • [17] T. Hachaj, M. R. Ogiela and M. Piekarczyk, The open online repository of karate motion capture data: A tool for scientists and sport educators, 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, 2017, pp. 1-5, doi: 10.1109/SSCI.2017.8285270
  • [18] Brown TN, Palmieri-Smith RM, McLean SG, Sex and limb differences in hip and knee kinematics and kinetics during anticipated and unanticipated jump landings: implications for anterior cruciate ligament injury, Br J Sports Med. 2009 Dec;43(13):1049-56. doi: 10.1136/bjsm.2008.055954. Epub 2009 Apr 16
  • [19] Sebastian Głowi´ nski, Tomasz Krzy˙zy´ nski, An inverse kinematic algorithm for the human leg, Journal of Theoretical and Applied Mechanics, 54, 1, pp. 53-61, Warsaw 2016, DOI: 10.15632/jtam-pl.54.1.53
  • [20] Kevin Adistambha, Christian H. Ritz, Ian Burnett, Motion classification using Dynamic Time Warping, Conference: International Workshop on Multimedia Signal Processing, MMSP 2008, October 8-10, 2008, Shangri-la Hotel, Cairns, Queensland, Australia
  • [21] Kerstin Witte, Peter Emmermacher, Sören Bystrzycki, Jana Potenberg, Movement structures of round kicks in karate, 25 International Symposium on Biomechanics in Sports (2007), pp. 302-305, ISSN 1999-4168
  • [22] Chen Lin Lee, Yu Fang Chin, Yu Liu, Comparing the difference between front-leg and back-leg round-house kicks attacking movement abilities in taekwondo, 23 International Symposium on Biomechanics in Sports (2005), pp. 877- 880, ISSN 1999- 4168
  • [23] Kong PW, Luk TC, Hong Y. Difference between Taekwondo roundhouse kick executed by the front and back leg—a biomechanical study. In: Hong Y, Johns DP, Sanders R, editors. Proceedings of the 18th International Symposium on Biomechanics in Sports. Hong Kong: International Symposium on Biomechanics in Sports 2000. p. 268–72.
  • [24] Emmermacher P, Witte K, Bystryzycki S and Potenberg J, Difference between taekwondo roundhouse kick executed by the front and back leg -a biomechanical study, 25 International Symposium on Biomechanics in Sports (2007), pp. 289-292, ISSN 1999-4168
  • [25] Takenori Obo, Chu Kiong Loo, Manjeevan Seera, Takahiro Takeda, Naoyuki Kubota, Arm motion analysis using genetic algorithm for rehabilitation and healthcare, Applied Soft Computing 52 (2017) 81–92
  • [26] Huiyu Zhou, Huosheng Hu, Nigel D. Harris, Wearable inertial sensors for arm motion tracking in home-based rehabilitation, Conference: Intelligent Autonomous Systems 9 - IAS-9, Proceedings of the 9th International Conference on Intelligent Autonomous Systems, University of Tokyo, Tokyo, Japan, March 7-9, 2006
  • [27] John Mitchell, Feroze Mahmood, Ruma Bose, Philip Hess, Vanessa Wong, Robina Matyal, Novel, Multimodal Approach for Basic Transesophageal Echocardiographic Teaching, Journal of cardiothoracic and vascular anesthesia, 28 (2014), doi: 10.1053/j.jvca.2014.01.006.
  • [28] Adam Piórkowski, Aleksander Kempny, The Transesophageal Echocardiography Simulator Based on Computed Tomography Images, IEEE transactions on bio-medical engineering (2013), doi: 60 292-299. 10.1109/TBME.2012.2226212.
  • [29] Robina Matyal, Feroze Mahmood, Ziyad Knio, Stephanie Jones, Lu Yeh, Rabina Amir, Ruma Bose, John Mitchell, Evaluation of the quality of transesophageal echocardiography images and verification of proficiency. Echo Research and Practice, 5, ERP-18 (2018), doi: 10.1530/ERP-18-0002.
  • [30] Robina Matyal, Mario Montealegre-Gallegos, John Mitchell, Han Kim, Remco Bergman, Katie Hawthorne, David O’Halloran, Vanessa Wong, Phillip Hess, Feroze Mahmood, Manual Skill Acquisition During Transesophageal Echocardiography Simulator Training of Cardiology Fellows: A Kinematic Assessment, Journal of Cardiothoracic and Vascular Anesthesia, 29 (2015) doi: 10.1053/j.jvca.2015.05.198.
  • [31] Mario Montealegre-Gallegos, Feroze Mahmood, Han Kim, Remco Bergman, Mitchell Remco, Bose John, Hawthorne Ruma, Katie M Hawthorne, David T O’Halloran, Vanessa Wong, Philip E Hess, Robina Matyal, Imaging skills for transthoracic echocardiography in cardiology fellows: The value of motion metrics, Annals of Cardiac Anaesthesia, 19 (2016), doi: 10.4103/0971-9784.179595
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-baba074e-d403-4c9b-b03e-163a39d06928
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