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Human Motion Video Analysis in Clinical Practice (Review)

Human Motion Video Analysis in Clinical Practice (Review)

Borzikov V.V., Rukina N.N., Vorobyova O.V., Kuznetsov A.N., Belova A.N.
Key words: biomechanics; video analysis; optical human motion; rehabilitation medicine.
2015, volume 7, issue 4, page 201.

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The development of new rehabilitation approaches to neurological and traumatological patients requires understanding of normal and pathological movement patterns. Biomechanical analysis of video images is the most accurate method of investigation and quantitative assessment of human normal and pathological locomotion. The review of currently available methods and systems of optical human motion analysis used in clinical practice is presented here. Short historical background is provided. Locomotion kinematics analysis using passive marker based systems is reviewed with special attention to the gait analysis. Clinical application of optical motion capture and analysis systems in the diagnosis of locomotion impairment, in Parkinson’s disease with movement control disorders, stroke sequelae, monitoring of motor function rehabilitation in patients with infantile cerebral paralysis, limb joint endo- and exoprosthetics and some other disorders is described.

  1. Jonsson H., Kärrholm J. Three-dimensional knee joint movements during a step-up: evaluation after cruciate ligament rupture. J Orthoped Res 1994; 12(6): 769–779, http://dx.doi.org/10.1002/jor.1100120604.
  2. Gavrila D.M. The visual analysis of human movement: a survey. Computer Vision and Image Understanding 1999; 73(1): 82–98, http://dx.doi.org/10.1006/cviu.1998.0716.
  3. Likhachev S.A., Lukashevich V.A. To the question of methods of motion video analysis application. Meditsinskie novosti 2008; 12: 38–44.
  4. Krishnan C., Washabaugh E.P., Seetharaman Y. A low cost real-time motion tracking approach using webcam technology. J Biomech 2015; 48(3): 544–548, http://dx.doi.org/10.1016/j.jbiomech.2014.11.048.
  5. Weber W., Weber E. Mechanik der menschlichen Gehwerkzeuge. Göttingen: Dieterich; 1836.
  6. Marey E. Animal mechanism: a treatise on terrestrial and aerial locomotion. London: Henry S. King & Co.; 1874, http://dx.doi.org/10.5962/bhl.title.84571.
  7. Muybridge E. Animal locomotion. Philadelphia: J.B. Lippincott Company; 1887.
  8. Braune W., Fischer O. Determination of the moments of inertia of the human body and its limbs. Springer-Verlag Berlin Heidelberg; 1988, http://dx.doi.org/10.1007/978-3-662-11236-6.
  9. Baker R. The history of gait analysis before the advent of modern computers. Gait Posture 2007; 26(3): 331–342, http://dx.doi.org/10.1016/j.gaitpost.2006.10.014.
  10. Bernshteyn N.A. O postroenii dvizheniy [About construction of movements]. Moscow: Gosudarstvennoe izdatel'stvo meditsinskoy literatury; 1947; 254 p.
  11. Romanov D.A. Upravlenie tekhnicheskoy podgotovlennost'yu sportsmenov na osnove komp'yuternogo videoanaliza dvizheniy. Dis. ... kand. ped. nauk [Management of sportsmen technical performance using computer-based motion video analysis. PhD Thesis]. Krasnodar; 2004.
  12. Cedras C., Shah M. Motion-based recognition a survey. Image and Vision Computing 1995; 13(2): 129–155, http://dx.doi.org/10.1016/0262-8856(95)93154-k.
  13. Gavrila D.M., Davis L.S. 3-D model-based tracking of humans in action: a multi-view approach. Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1996; p. 73–80, http://dx.doi.org/10.1109/cvpr.1996.517056.
  14. Furnée H. Real-time motion capture systems. In: Allard P., Cappozzo A., Lundberg A., Vaughan C.L. (editors). Three-dimensional analysis of human locomotion. Chichester, UK: John Wiley & Sons; 1997; p. 85–108.
  15. Human motion analysis: current applications and future directions. Harris G.F., Smith P.A. (editors). New York: IEEE Press; 1996.
  16. Wren C.R., Azarbayejani A., Darrell T., Pentland A.P. Pfinder: real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997; 19(7): 780–785, http://dx.doi.org/10.1109/34.598236.
  17. Kakadiaris I.A., Metaxas D. 3D human body model acquisiton from multiple views. Proceedings of IEEE International Conference on Computer Vision 1995, http://dx.doi.org/10.1109/iccv.1995.466881.
  18. Narayanan P.J., Rander P., Kanade T. Technical Report CMU-RI-TR-95-25. Robotics Institute Carnegie Mellon University; 1995. Synchronous capture of image sequences from multiple cameras.
  19. Karaulova I.A., Hall P.M., Marshall A.D. Tracking people in three dimensions using a hierarchical model of dynamics. Image and Vision Computing 2002; 20: 691–700, http://dx.doi.org/10.1016/s0262-8856(02)00059-8.
  20. Sigal L., Balan A.O., Black M.J. HumanEva: synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. International Journal of Computer Vision 2009; 87(1–2): 4–27, http://link.springer.com/article/10.1007%2Fs11263-009-0273-6.
  21. Hogg D. Model-based vision: a program to see a walking person. Image and Vision Computing 1983; 1(1): 5–20, http://dx.doi.org/10.1016/0262-8856(83)90003-3.
  22. Lee H.J., Chen Z. Determination of 3D human body posture from a single view. Computer Vision, Graphics, and Image Processing 1985; 29(3): 396, http://dx.doi.org/10.1016/0734-189x(85)90137-9.
  23. Lafortune M.A., Cavanagh P.R., Sommer H.J., Kalenak A. Three-dimensional kinematics of the human knee during walking. J Biomechanics 1992; 25(4): 347–357, http://dx.doi.org/10.1016/0021-9290(92)90254-x.
  24. de Vries W.H.K., Veeger H.E.J., Baten C.T.M., van der Helm F.C.T. Magnetic distortion in motion labs, implications for validating inertial magnetic sensors. Gait Posture 2009; 29(4): 535–541, http://dx.doi.org/10.1016/j.gaitpost.2008.12.004.
  25. Engin M., Demirel A., Engin E.Z., Fedakar M. Recent developments and trends in biomedical sensors. Measurement 2005; 37(2): 173–188, http://dx.doi.org/10.1016/j.measurement.2004.11.002.
  26. Ghoussayni S., Stevens C., Durham S., Ewins D. Assessment and validation of a simple automated method for the detection of gait events and intervals. Gait Posture 2004; 20(3): 266–272, http://dx.doi.org/10.1016/j.gaitpost.2003.10.001.
  27. Kwon D.Y., Gross M. Combining body sensors and visual sensors for motion training. Proceedings of the 2005 ACM SIGCHI International Conference on Advances in Computer Entertainment Technology; Valencia, Spain 15–17 Jun 2005; p. 94–101, http://dx.doi.org/10.1145/1178477.1178490.
  28. Tao W., Liu T., Zheng R., Feng H. Gait analysis using wearable sensors. Sensors (Basel) 2012; 12(2): 2255–2283, http://dx.doi.org/10.3390/s120202255.
  29. Andriacchi T.P., Alexander E.J. Studies of human locomotion: past, present and future. J Biomech 2000; 33(10): 1217–1224, http://dx.doi.org/10.1016/S0021-9290(00)00061-0.
  30. Moeslund T.B., Granum E. A survey of computer vision-based human motion capture. Computer Vision and Image Understanding 2001; 81(3): 231–268, http://dx.doi.org/10.1006/cviu.2000.0897.
  31. Andriacchi T.P., Ogle J.A., Galante J.O. Walking speed as a basis for normal and abnormal gait measurements. J Biomech 1977; 10(4): 261–268, http://dx.doi.org/10.1016/0021-9290(77)90049-5.
  32. Spoor C.W., Veldpaus F.E. Rigid body motion calculated from spatial co-ordinates of markers. J Biomech 1980; 13(4): 391–393, http://dx.doi.org/10.1016/0021-9290(80)90020-2.
  33. Ferrigno G., Pedotti A. Elite: a digital dedicated hardware system for movement analysis via real-time TV signal processing. IEEE Trans Biomed Eng 1985; 32(11): 943–950, http://dx.doi.org/10.1109/tbme.1985.325627.
  34. Dotsenko V.I., Voronov A.V., Titarenko N.Yu., Titarenko K.E. Computer-based motion video analysis in sports medicine and neurorehabilitation. Meditsinskiy alfavit 2005 3(41): 12–14.
  35. Cappozzo A., Della Croce U., Leardini A., Chiari L. Human movement analysis using stereophotogrammetry. Part 1: theoretical background. Gait Posture 2005; 21(2): 186–196, http://dx.doi.org/10.1016/j.gaitpost.2004.01.010.
  36. Wang L., Hu W., Tan T. Recent developments in human motion analysis. Pattern Recognition 2003; 36(3): 585–601, http://dx.doi.org/10.1016/s0031-3203(02)00100-0.
  37. Isard M., Blake A. Visual tracking by stochastic propagation of conditional density. In: Proceeding of the 4th European Conference on Computer Vision. New York: 1996; p. 343–356.
  38. Bregler C., Malik J. Tracking people with twists and exponential maps. Proceedings 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1998, http://dx.doi.org/10.1109/cvpr.1998.698581.
  39. Ma Y., Soatto S., Košecká J., Sastry S. An invitation to 3D vision. Interdisciplinary Applied Mathematics. Springer New York; 2004, http://dx.doi.org/10.1007/978-0-387-21779-6.
  40. Cappozzo A., Cappello A., Croce U.D., Pensalfini F. Surface-marker cluster design criteria for 3-D bone movement reconstruction. IEEE Trans Biomed Eng 1997; 44(12): 1165–1174, http://dx.doi.org/10.1109/10.649988.
  41. Ceseracciu E., Sawacha Z., Cobelli C. Comparison of markerless and marker-based motion capture technologies through simultaneous data collection during gait: proof of concept. PLoS One 2014; 9(3): e87640, http://dx.doi.org/10.1371/journal.pone.0087640.
  42. Mündermann L., Corazza S., Andriacchi T. The evolution of methods for the capture of human movement leading to markerless motion capture for biomechanical applications. J Neuroeng Rehabil 2006; 3: 6, http://dx.doi.org/10.1186/1743-0003-3-6.
  43. Vlasenko V.P. Tekhnologiya “Motion Capture”. Periferiynye ustroystva [Technology “Motion Capture”. Peripherals]. Zaporizhia; 2007. URL: http://www.bestreferat.ru/referat-401678.html.
  44. Lanshammar H., Persson T., Medved V. Comparison between a marker-based and a marker-free method to estimate centre of rotation using video image analysis. In: Second World Congress of Biomechanics. Amsterdam; 1994.
  45. Besl P., McKay N. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 1992; 14(2): 239–256, http://dx.doi.org/10.1109/34.121791.
  46. Aggarwal J., Cai Q. Human motion analysis: a review. Computer Vision and Image Understanding 1999; 73(3): 428–440, http://dx.doi.org/10.1006/cviu.1998.0744.
  47. Skvortsov D.V. The methods of investigation of kinematics and modern standards. Videoanalysis. Lechebnaya fizkul’tura i sportivnaya meditsina 2012; 12: 4–10.
  48. Kent J., Franklyn-Miller A. Biomechanical models in the study of lower limb amputee kinematics: a review. Prosthet Orthot Int 2011; 35(2): 124–39, http://dx.doi.org/10.1177/0309364611407677.
  49. Andriacchi T.P., Alexander E.J., Toney M.K., Dyrby C., Sum J. A point cluster method for in vivo motion analysis: applied to a study of knee kinematics. J Biomech Eng 1998; 120(6): 743–749, http://dx.doi.org/10.1115/1.2834888.
  50. Zakotnik J., Matheson T., Dürr V. A posture optimization algorithm for model-based motion capture of movement sequences. J Neurosci Methods 2004; 135(1–2): 43–54, http://dx.doi.org/10.1016/j.jneumeth.2003.11.013.
  51. Lu T.-W., O’Connor J.J. Bone position estimation from skin marker coordinates using global optimization with joint constraints. J Biomech 1999; 32(2): 129–134, http://dx.doi.org/10.1016/s0021-9290(98)00158-4.
  52. Herda L., Fua P., Plänkers R., Boulic R., Thalmann D. Using skeleton-based tracking to increase the reliability of optical motion capture. Hum Mov Sci 2001; 20(3): 313–341, http://dx.doi.org/10.1016/s0167-9457(01)00050-1.
  53. Royo Sánchez A.C., Aguilar Martín J.J., Santolaria Mazo J. Development of a new calibration procedure and its experimental validation applied to a human motion capture system. J Biomech Eng 2014; 136(12): 124502, http://dx.doi.org/10.1115/1.4028523.
  54. Romkes J., Rudmann C., Brunner R. Changes in gait and EMG when walking with the Masai Barefoot Technique. Clin Biomech 2006; 21(1): 75–81, http://dx.doi.org/10.1016/j.clinbiomech.2005.08.003.
  55. Wu G., Cavanagh P. ISB recommendation for stan-dardization in the reporting of kinematic data. J Biomech 1995; 28(10): 1257–1261, http://dx.doi.org/10.1016/0021-9290(95)00017-c.
  56. Grood E.S., Suntay W.J. A joint coordinate system for the clinical description of three-dimensional motions: application to the knee. J Biomech Eng 1983; 105(2): 136–144, http://dx.doi.org/10.1115/1.3138397.
  57. Wu G., van der Helm F.C., Veeger H.E., Makhsous M., Van Roy P., Anglin C., Nagels J., Karduna A.R., McQuade K., Wang X., Werner F.W., Buchholz B. ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion — part II: shoulder, elbow, wrist and hand. J Biomech 2005; 38(5): 981–992, http://dx.doi.org/10.1016/j.jbiomech.2004.05.042.
  58. Wu G., Siegler S., Allard P., Kirtley C., Leardini A., Rosenbaum D., Whittle M., D’Lima D.D., Cristofolini L., Witte H., Schmid O., Stokes I. ISB recommendation on definitions of joint coordinate system of various joints for the reporting of human joint motion — part I: ankle, hip, and spine. J Biomech 2002; 35(4): 543–548.
  59. Gorton G.E., Hebert D.A., Gannotti M.E. Assessment of the kinematic variability among 12 motion analysis laboratories. Gait and Posture 29(3): 398–402, http://dx.doi.org/10.1016/j.gaitpost.2008.10.060.
  60. Eng J.J., Winter D.A. Kinetic analysis of the lower limb during walking: what information can be gained from a three-dimensional model? J Biomech 1995; 28(6): 753–758, http://dx.doi.org/10.1016/0021-9290(94)00124-m.
  61. Simon R.S. Quantification of human motion: gait analysis-benefits and limitations to its application to clinical problems. J Biomech 2004; 37(12): 1869–1880, http://dx.doi.org/10.1016/j.jbiomech.2004.02.047.
  62. Chau T. A review of analytical techniques for gait data. Part 1: fuzzy, statistical and fractal methods. Gait Posture 2001; 13(1): 49–66, http://dx.doi.org/10.1016/S0966-6362(00)00094-1.
  63. Inman V., Ralston H., Todd F. Human walking. Baltimore: Williams & Wilkins; 1981.
  64. Perry J., Thorofare S., Jon D. Gait analysis: normal and pathological function. JAMA 1992; 268(22): 3257, http://dx.doi.org/10.1097/01241398-199211000-00023.
  65. Winter D.A. Kinematic and kinetic patterns in human gait: variability and compensating effects. Hum Mov Sci 1984; 3(1–2): 51–76, http://dx.doi.org/10.1016/0167-9457(84)90005-8.
  66. Voronov A.V., Dotsenko V.I., Titarenko K.E., Titarenko N.Yu. Komp’yuternyy videoanaliz dvizheniy v nauchnykh issledovaniyakh i klinicheskoy praktike. V kn.: Sotsial'naya pediatriya: sbornik nauchnykh trudov [Computer-based motion video analysis in scientific researches and clinical practice. In: Social pediatrics: collection of scientific proceedings]. Kiev; 2005.
  67. Stokic D.S., Horn T.S., Ramshur J.M., Chow J.W. Agreement between temporospatial gait parameters of an electronic walkway and a motion capture system in healthy and chronic stroke populations. Am J Phys Med Rehabil 2009; 88(6): 437–444, http://dx.doi.org/10.1097/PHM.0b013e3181a5b1ec.
  68. Whittle M.W., Barnes S.C. Defining normal ranges for gait parameter. In: Gait Anal Med Photogramm. Vol. 1–3. Oxford, Headington; 1987; p. 46–47.
  69. Ferrari A., Benedetti M.G., Pavan E., Frigo C., Bettinelli D., Rabuffetti M., Crenna P., Leardini A. Quantitative comparison of five current protocols in gait analysis. Gait Posture 2008; 28(2): 207–216, http://dx.doi.org/10.1016/j.gaitpost.2007.11.009.
  70. Leardini A., Sawacha Z., Paolini G., Ingrosso S., Nativo R., Benedetti M.G. A new anatomically based protocol for gait analysis in children. Gait Posture 2007; 26(4): 560–571, http://dx.doi.org/10.1016/j.gaitpost.2006.12.018.
  71. Zhao S., Chen Y.S., Zhang X.L. Clinical application of gait analysis in hip arthroplasty. Orthop Surg 2010; 2(2): 94–99, http://dx.doi.org/10.1111/j.1757-7861.2010.00070.x.
  72. Lawson B.E., Huff A., Goldfarb M. A preliminary investigation of powered prostheses for improved walking biomechanics in bilateral transfemoral amputees. Conf Proc IEEE Eng Med Biol Soc 2012; 2012: 4164–4167, http://dx.doi.org/10.1109/EMBC.2012.6346884.
  73. McInnes K.A., Younger A.S., Oxland T.R. Initial instability in total ankle replacement: a cadaveric biomechanical investigation of the STAR and agility prostheses. J Bone Joint Surg Am 2014; 96(17): e147, http://dx.doi.org/10.2106/JBJS.L.01690.
  74. Alradwan H., Khan M., Grassby M.H., Bedi A., Philippon M.J., Ayeni O.R. Gait and lower extremity kinematic analysis as an outcome measure after femoroacetabular impingement surgery. Arthroscopy 2015; 31(2): 339–344, http://dx.doi.org/10.1016/j.arthro.2014.06.016.
  75. Gribble P., Robinson R. Alterations in knee kinematics and dynamic stability associated with chronic ankle instability. J Athl Train 2009; 44(4): 350–355, http://dx.doi.org/10.4085/1062-6050-44.4.350.
  76. Kim C.M., Eng J.J. Magnitude and pattern of 3D kinematic and kinetic gait profiles in persons with stroke: relationship to walking speed. Gait Posture 2004; 20(2): 140–146, http://dx.doi.org/10.1016/j.gaitpost.2003.07.002.
  77. Das S., Trutoiu L., Murai A., Alcindor D., Oh M., De la Torre F., Hodgins J. Quantitative measurement of motor symptoms in Parkinson’s disease: a study with full-body motion capture data. Conf Proc IEEE Eng Med Biol Soc 2011; 2011: 6789–6792, http://dx.doi.org/10.1109/IEMBS.2011.6091674.
  78. Cedervall Y., Halvorsen K., Aberg A.C. A longitudinal study of gait function and characteristics of gait disturbance in individuals with Alzheimer’s disease. Gait Posture 2014; 39(4): 1022–1027, http://dx.doi.org/10.1016/j.gaitpost.2013.12.026.
  79. Likhachev S.A., Lukashevich V.A., Khromenkov A.V. The video motion analysis as a method of the diagnosis of the basal ganglion lesion in Parkinson’s disease. Zhurnal nevrologii i psikhiatrii im. S.S. Korsakova 2011; 111(12): 44–47.
  80. Likhachov S.A., Lukashevich V.A. Videoanalysis of step movement: phenomenology of visual estimation. Mezhdunarodnyy nevrologicheskiy zhurnal 2012; 2: 178–182.
  81. Jerome G.J., Ko S.U., Kauffman D., Studenski S.A., Ferrucci L., Simonsick E.M. Gait characteristics associated with walking speed decline in older adults: results from the Baltimore Longitudinal Study of Aging. Arch Gerontol Geriatr 2015; 60(2): 239–243, http://dx.doi.org/10.1016/j.archger.2015.01.007.
  82. Sawacha Z., Gabriella G., Cristoferi G., Guiotto A., Avogaro A., Cobelli C. Diabetic gait and posture abnormalities: a biomechanical investigation through three dimensional gait analysis. Clin Biomech (Bristol, Avon) 2009; 24(9): 722–728, http://dx.doi.org/10.1016/j.clinbiomech.2009.07.007.
  83. Titarenko N.Yu., Voronov A.V., Semenova K.A., Dotsenko V.I., Titarenko K.E., Levchenkova V.D., Politova I.Ya. Computer-based motion video analysis in the assessment of rehabilitation treatment of children with residual neuromotor deficiency. Funktsional’naya diagnostika 2006; 3: 69–75.
  84. Titarenko N.Iu., Voronov A.V. The effect of the reflex-load device Gravistat/Graviton on walk stereotype in patients with spastic diplegia. Zhurnal nevrologii i psikhiatrii im. S.S. Korsakova 2012; 111(7–2): 18–23.
  85. Tsai C.Y., Hogaboom N.S., Boninger M.L., Koontz A.M. The relationship between independent transfer skills and upper limb kinetics in wheelchair users. Biomed Res Int 2014; 2014: 984526, http://dx.doi.org/10.1155/2014/984526.
  86. Ropars M., Cretual A., Thomazeau H., Kaila R., Bonan I. Volumetric definition of shoulder range of motion and its correlation with clinical signs of shoulder hyperlaxity. A motion capture study. J Shoulder Elbow Surg 2015; 24(2): 310–316, http://dx.doi.org/10.1016/j.jse.2014.06.040.
  87. Crétual A., Bonan I., Ropars M. Development of a novel index of shoulder’s mobility based on the configuration space volume and its link to mono-axial amplitudes. Man Ther 2015; 20(3): 433–439, http://dx.doi.org/10.1016/j.math.2014.10.020.
  88. Maier M.W., Kasten P., Niklasch M., Dreher T., Zeifang F., Rettig O., Wolf S.I. 3D motion capture using the HUX model for monitoring functional changes with arthroplasty in patients with degenerative osteoarthritis. Gait Posture 2014; 39(1): 7–11, http://dx.doi.org/10.1016/j.gaitpost.2013.07.111.
  89. Hebert J.S., Lewicke J., Williams T.R., Vette A.H. Normative data for modified Box and Blocks test measuring upper-limb function via motion capture. J Rehabil Res Dev 2014; 51(6): 918–932, http://dx.doi.org/10.1682/JRRD.2013.10.0228.
  90. Buffi J.H., Sancho Bru J.L., Crisco J.J., Murray W.M. Evaluation of hand motion capture protocol using static computed tomography images: application to an instrumented glove. J Biomech Eng 2014; 136(12): 124501, http://dx.doi.org/10.1115/1.4028521.
  91. Jagos H., Oberzaucher J., Reichel M., Zagler W.L., Hlauschek W. A multimodal approach for insole motion measurement and analysis. Procedia Eng 2010; 2(2): 3103–3108, http://dx.doi.org/10.1016/j.proeng.2010.04.118.
  92. Miller A.L. A new method for synchronization of motion capture and plantar pressure data. Gait Posture 2010; 32(2): 279–381, http://dx.doi.org/10.1016/j.gaitpost.2010.04.012.
  93. Martin C., Bideau B., Bideau N., Nicolas G., Delamarche P., Kulpa R. Energy flow analysis during the tennis serve: comparison between injured and noninjured tennis players. Am J Sports Med 2014; 42(11): 2751–2760, http://dx.doi.org/10.1177/0363546514547173.
  94. Raychoudhury S., Hu D., Ren L. Three-dimensional kinematics of the human metatarsophalangeal joint during level walking. Front Bioeng Biotechnol 2014; 2: 73, http://dx.doi.org/10.3389/fbioe.2014.00073.
  95. Seel Т., Raisch J., Schauer Т. IMU-based joint angle measurement for gait analysis. Sensors (Basel) 2014; 14(4): 6891–6909, http://dx.doi.org/10.3390/s140406891.
  96. Corazza S., Mündermann L., Chaudhari A., Demattio T., Cobelli C., Andriacchi T.P. A markerless motion capture system to study musculoskeletal biomechanics: visual hull and simulated annealing approach. Ann Biomed Eng 34(6): 1019–1029, http://dx.doi.org/10.1007/s10439-006-9122-8.
  97. Lenar J., Witkowski M., Carbone V., Kolk S., Adamázyk M., Sitnik R., van der Krogt M., Verdonschot N. Lower body kinematics evaluation based on a multidirectional four-dimensional structured light measurement. J Biomed Opt 2013; 18(5): 56014, http://dx.doi.org/10.1117/1.JBO.18.5.056014.
  98. Belyea B.C., Lewis E., Gabor Z., Jackson J., King D.L. Validity and intra-rater reliability of 2-dimensional motion analysis using a hand-held tablet compared to traditional 3-dimensional motion analysis. J Sport Rehabil 2015, http://dx.doi.org/10.1123/jsr.2014-0194.
  99. Bonnet V., Sylla N., Cherubini A., Gonzáles A., Azevedo Coste C., Fraisse P., Venture G. Toward an affordable and user-friendly visual motion capture system. Conf Proc IEEE Eng Med Biol Soc 2014; 2014: 3634–3637, http://dx.doi.org/10.1109/EMBC.2014.6944410.
  100. Lin H.-I., Lin Y.-H. A novel teaching system for industrial robots. Sensors (Basel) 2014; 14(4): 6012–6031, http://dx.doi.org/10.3390/s140406012.
Borzikov V.V., Rukina N.N., Vorobyova O.V., Kuznetsov A.N., Belova A.N. Human Motion Video Analysis in Clinical Practice (Review). Sovremennye tehnologii v medicine 2015; 7(4): 201, https://doi.org/10.17691/stm2015.7.4.26


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