A Survey of Knee Osteoarthritis Assessment Based on Gait

Archives of Computational Methods in Engineering volume 28, pages345–385(2021)Cite this article

Abstract

In today’s era of new advancements, diagnosing a pathology at an early stage has given rise to the development of automated diagnostic systems. Knee Osteoarthritis (KOA) being among one of the most painful joint disorders is the root cause for disability, particularly in elderly population. Gait based recognition of KOA is a prominent area that requires deliberations from the end of researchers, academicians and scientists to develop more automated systems that not only offer reliability and accuracy but are also affordable for common man. This article aims to provide an in-depth investigation of efforts directed towards vision-based, sensor-based and hybrid KOA identification. The study is based on the historical data gathered and background obtained viz-a-viz clinical gait analysis. An extensive survey of KOA gait acquisition modalities and feature representation approaches for the purpose of critically examining them are also presented. The study surveys the statistical metrics used for evaluating KOA, considering relevant articles. Based on the survey, this article aims to provide an up-to-date review of machine learning techniques for classification of KOA and healthy subjects. Furthermore, this article also identifies open research challenges existing in the literature that could be explored further for providing more effective KOA analysis. Finally, this article presents the future perspectives and provides an outline of the proposed work for efficient KOA diagnosis based on vision-based gait.

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References

  1. 1.

    Gupta D, Sundaram S, Khanna A, Hassanien AE, de Albuquerque VHC (2018) Improved diagnosis of Parkinson’s disease based on optimized Crow Search Algorithm. Comput Electr Eng 68:412–424. https://doi.org/10.1016/j.compeleceng.2018.04.014

    Article  Google Scholar 

  2. 2.

    Wang Y, Wang A-N, Ai Q, Sun H-J (2017) An adaptive kernel-based weighted extreme learning machine approach for effective detection of Parkinson’s disease. Biomed Signal Process Control 38:400–410. https://doi.org/10.1016/j.bspc.2017.06.015

    Article  Google Scholar 

  3. 3.

    Lahmiri S (2017) Parkinson’s disease detection based on dysphonia measurements. Phys A 471:98–105. https://doi.org/10.1016/j.physa.2016.12.009

    Article  Google Scholar 

  4. 4.

    Nilashi M, Ibrahim O, Ahmadi H, Shahmoradi L, Farahmand M (2018) A hybrid intelligent system for the prediction of Parkinson’s Disease progression using machine learning techniques. Biocybern Biomed Eng 38(1):1–15. https://doi.org/10.1016/j.bbe.2017.09.002

    Article  Google Scholar 

  5. 5.

    Srivastava A, Goyal V, Sood SK, Sharma R (2018) Reduced saccadic velocity and pupillary width in young onset Parkinson’s disease. Neurol Psychiatry Brain Res 37:17–20. https://doi.org/10.1016/j.npbr.2017.12.005

    Article  Google Scholar 

  6. 6.

    Kale A et al (2004) Identification of humans using gait. IEEE Trans Image Process 13(9):1163–1173. https://doi.org/10.1109/TIP.2004.832865

    Article  Google Scholar 

  7. 7.

    Wang L, Tan T, Ning H, Hu W (2003) Silhouette analysisbased gait recognition for human identification. IEEE Trans Pattern Anal Mach Intell 25(12):1505–1518. https://doi.org/10.1109/TPAMI.2003.1251144

    Article  Google Scholar 

  8. 8.

    Gornale SS, Patravali PU, Manza RR (2016) A survey on exploration and classification of osteoarthritis using image processing techniques. Int J Sci Eng Res 7(6):334–356

    Google Scholar 

  9. 9.

    Stamford JA, Schmidt PN, Friedl KE (2015) What engineering technology could do for quality of life in parkinson’s disease: a review of current needs and opportunities. IEEE J Biomed Health Inform 19(6):1862–1872. https://doi.org/10.1109/JBHI.2015.2464354

    Article  Google Scholar 

  10. 10.

    Kotti M, Duffell LD, Faisal AA, McGregor AH (2017) Detecting knee osteoarthritis and its discriminating parameters using random forests. Med Eng Phys 43:19–29. https://doi.org/10.1016/j.medengphy.2017.02.004

    Article  Google Scholar 

  11. 11.

    Debi R et al (2009) Differences in gait patterns, pain, function and quality of life between males and females with knee osteoarthritis: a clinical trial. BMC Musculoskelet Disord 10(1):127. https://doi.org/10.1186/1471-2474-10-127

    Article  Google Scholar 

  12. 12.

    Tarnita D, Catana M, Tarnita DN (2013) Experimental measurement of flexion-extension movement in normal and osteoarthritic human knee. Rom J Morphol Embryol 54(2):309–313

    Google Scholar 

  13. 13.

    Favre J, Erhart-Hledik JC, Andriacchi TP (2014) Age-related differences in sagittal-plane knee function at heel-strike of walking are increased in osteoarthritic patients. Osteoarthr Cartil 22(3):464–471. https://doi.org/10.1016/j.joca.2013.12.014

    Article  Google Scholar 

  14. 14.

    Creaby MW, Bennell KL, Hunt MA (2012) Gait differs between unilateral and bilateral knee osteoarthritis. Arch Phys Med Rehabil 93(5):822–827. https://doi.org/10.1016/j.apmr.2011.11.029

    Article  Google Scholar 

  15. 15.

    Kobsar D, Osis ST, Boyd JE, Hettinga BA, Ferber R (2017) Wearable sensors to predict improvement following an exercise intervention in patients with knee osteoarthritis. J Neuroeng Rehabil 14(1):94. https://doi.org/10.1186/s12984-017-0309-z

    Article  Google Scholar 

  16. 16.

    Rutherford DJ, Baker M (2018) Knee moment outcomes using inverse dynamics and the cross product function in moderate knee osteoarthritis gait: a comparison study. J Biomech 78:150–154. https://doi.org/10.1016/j.jbiomech.2018.07.021

    Article  Google Scholar 

  17. 17.

    Spasojevic S et al (2015) A vision-based system for movement analysis in medical applications: the example of parkinson disease. In: 10th international conference on computer vision systems, Denmark, pp 424–434, Jul 2015. https://doi.org/10.1007/978-3-319-20904-3_38

  18. 18.

    Armand S, Decoulon G, Bonnefoy-Mazure A (2016) Gait analysis in children with cerebral palsy. Effort Open Rev 1(12):448–460. https://doi.org/10.1302/2058-5241.1.000052

    Article  Google Scholar 

  19. 19.

    Sanders RD, Gillig PM (2010) Gait and its assessment in psychiatry. Psychiatry Neurol 7(7):38–43. https://doi.org/10.1017/CBO9781139192309.004

    Article  Google Scholar 

  20. 20.

    Pirker W, Katzenschlager R (2017) Gait disorders in adults and the elderly. Wien Klin Wochensch 129(3–4):81–95. https://doi.org/10.1007/s00508-016-1096-4

    Article  Google Scholar 

  21. 21.

    da Silva-Hamu TCD et al (2013) The impact of obesity in the kinematic parameters of gait in young women. Int J Gen Med 6:507–513. https://doi.org/10.2147/IJGM.S44768

    Article  Google Scholar 

  22. 22.

    Roser M, Ritchie H (2018) Burden of disease (online). https://ourworldindata.org/burden-of-disease. Accessed 28 Oct 2018

  23. 23.

    Woolf AD (2015) Global burden of osteoarthritis and musculoskeletal diseases. BMC Musculoskelet Disord 16(1):S3. https://doi.org/10.1186/1471-2474-16-S1-S3

    Article  Google Scholar 

  24. 24.

    World Health Organization (2018) Musculoskeletal conditions: Feb 2018 (online). https://www.who.int/mediacentre/factsheets/musculoskeletal/en/. Accessed 12 Sep 2018

  25. 25.

    Hoy D et al (2014) The global burden of low back pain: estimates from the Global Burden of Disease 2010 study. Ann Rheum Disord 73(6):968–974. https://doi.org/10.1136/annrheumdis-2013-204428

    Article  Google Scholar 

  26. 26.

    Storheim K, Zwart J-A (2014) Musculoskeletal disorders and the Global Burden of Disease study. Ann Rheum Disord 73(6):949–950. https://doi.org/10.1136/annrheumdis-2014-205327

    Article  Google Scholar 

  27. 27.

    Lancet T (2016) Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Global Health Metr 388:1545–1602. https://doi.org/10.1016/S0140-6736(16)31678-6

    Article  Google Scholar 

  28. 28.

    Cross M et al (2014) The global burden of hip and knee osteoarthritis: estimates from the Global Burden of Disease 2010 study. Ann Rheum Disord 73(7):1323–1330. https://doi.org/10.1136/annrheumdis-2013-204763

    Article  Google Scholar 

  29. 29.

    Kaufman KR, Hughes C, Morrey BF, Morrey M, An KN (2001) Gait characteristics of patients with knee osteoarthritis. J Biomech 34(7):907–915. https://doi.org/10.1016/S0021-9290(01)00036-7

    Article  Google Scholar 

  30. 30.

    Vos T et al (2012) Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380:2163–2196. https://doi.org/10.1016/S0140-6736(12)61729-2

    Article  Google Scholar 

  31. 31.

    Ishikawa Y et al (2017) Gait analysis of patients with knee osteoarthritis by using elevation angle: confirmation of the planar law and analysis of angular difference in the approximate plane. Adv Robot 31(1–2):68–79. https://doi.org/10.1080/01691864.2016.1229217

    Article  Google Scholar 

  32. 32.

    Cui X, Zhao Z, Ma C, Chen F, Liao H (2018) A Gait character analyzing system for osteoarthritis pre-diagnosis using RGB-D camera and supervised classifier. In: World congress on medical physics and biomedical engineering, IFMBE proceedings, 2018, pp 297–301. https://doi.org/10.1007/978-981-10-9035-6_53

  33. 33.

    Gait analysis to detect Alzheimer’s disease: May 14, 2018 (online). https://www.dr-hempel-network.com/digital-health-technolg y/gait-analysis-to-detect-alzheimers-disease%E2%80%8B/

  34. 34.

    Levinger P et al (2007) The application of multiclass SVM to the detection of knee pathologies using kinetic data: a preliminary study. In: 3rd International conference on intelligent sensors, sensor networks and information, Australia, pp. 589–594. https://doi.org/10.1109/issNIP.2007.4496909

  35. 35.

    Asay JL, Boyer KA, Andriacchi TP (2013) Repeatability of gait analysis for measuring knee osteoarthritis pain in patients with severe chronic pain. J Orthop Res 31(7):1007–1012. https://doi.org/10.1002/jor.22228

    Article  Google Scholar 

  36. 36.

    Metcalfe AJ et al (2013) The effect of osteoarthritis of the knee on the biomechanics of other joints in the lower limbs. Bone Joint J 95(3):348–353. https://doi.org/10.1302/0301-620X.95B3.30850

    Article  Google Scholar 

  37. 37.

    Verlekar TT, Soares LD, Correia PL (2018) Automatic classification of gait impairments using a markerless 2D video-based system. Sensors 18(9):2743. https://doi.org/10.3390/s18092743

    Article  Google Scholar 

  38. 38.

    Astephen Wilson JL, Deluzio KJ, Dunbar MJ, Caldwell GE, Hubley-Kozey CL (2011) The association between knee joint biomechanics and neuromuscular control and moderate knee osteoarthritis radiographic and pain severity. Osteoarthr Cartil 19(2):186–193. https://doi.org/10.1016/j.joca.2010.10.020

    Article  Google Scholar 

  39. 39.

    Astephen JL, Deluzio KJ (2005) Changes in frontal plane dynamics and the loading response phase of the gait cycle are characteristic of severe knee osteoarthritis application of a multidimensional analysis technique. Clin Biomech 20(2):209–217. https://doi.org/10.1016/j.clinbiomech.2004.09.007

    Article  Google Scholar 

  40. 40.

    Farrokhi S, O’Connell M, Gil AB, Sparto PJ, Kelley Fitzgerald G (2015) Altered gait characteristics in individuals with knee osteoarthritis and self-reported knee instability. J Orthop Sports Phys Ther 45(5):351–359. https://doi.org/10.2519/jospt.2015.5540

    Article  Google Scholar 

  41. 41.

    Phinyomark A, Osis ST, Hettinga BA, Kobsar D, Ferber R (2016) Gender differences in gait kinematics for patients with knee osteoarthritis. BMC Musculoskelet Disord 17:157. https://doi.org/10.1186/s12891-016-1013-z

    Article  Google Scholar 

  42. 42.

    Brunton LR et al (2012) Inertial sensor based gait analysis: a clinical application in patients with osteoarthritis. Osteoarthr Cartil 20(1):S107. https://doi.org/10.1016/j.joca.2012.02.121

    MathSciNet  Article  Google Scholar 

  43. 43.

    Hubley-Kozey CL, Astephen Wilson JL, Costello KE, Stanish WD (2015) Biomechanical and neuromuscular alterations in knee osteoarthritis and asymptomatic controls: a longitudinal study. Osteoarthr Cartil 23(2):A99–A100. https://doi.org/10.1016/j.joca.2015.02.810

    Article  Google Scholar 

  44. 44.

    Elbaz A et al (2014) Novel classification of knee osteoarthritis severity based on spatiotemporal gait analysis. Osteoarthr Cartil 22(3):457–463. https://doi.org/10.1016/j.joca.2013.12.015

    Article  Google Scholar 

  45. 45.

    Sims EL et al (2009) Sex differences in biomechanics associated with knee osteoarthritis. J Women Aging 21(3):159–170. https://doi.org/10.1080/08952840903054856

    Article  Google Scholar 

  46. 46.

    Deluzio KJ, Astephen JL (2007) Biomechanical features of gait waveform data associated with knee osteoarthritis: an application of principal component analysis. Gait Posture 25(1):86–93. https://doi.org/10.1016/j.gaitpost.2006.01.007

    Article  Google Scholar 

  47. 47.

    Mahmoudian A et al (2017) Changes in gait characteristics of women with early and established medial knee osteoarthritis: results from a 2-years longitudinal study. Clin Biomech 50:32–39. https://doi.org/10.1016/j.clinbiomech.2017.10.004

    Article  Google Scholar 

  48. 48.

    Munoz-Organero M et al (2017) Identification of walking strategies of people with osteoarthritis of the knee using insole pressure sensors. IEEE Sens J 17(12):3909–3920. https://doi.org/10.1109/JSEN.2017.2696303

    Article  Google Scholar 

  49. 49.

    Prakesh C, Kumar R, Mittal N (2018) Recent developments in human gait research: parameters, approaches, applications, machine learning techniques and challenges. Artif Intell Rev 49(1):1–40. https://doi.org/10.1007/s10462-016-9514-6

    Article  Google Scholar 

  50. 50.

    Tao W, Liu T, Zheng R, Feng H (2012) Gait analysis using wearable sensors. Sensors 12(2):2255–2283. https://doi.org/10.3390/s120202255

    Article  Google Scholar 

  51. 51.

    Muro-de-la-Herran A, Garcia-Zapirain B, Mendez-Zorrilla A (2014) Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications. Sensors 14(2):3362–3394. https://doi.org/10.3390/s140203362

    Article  Google Scholar 

  52. 52.

    Ali A, Sundaraj K, Ahmad B, Ahamed N, Islam A (2012) Gait disorder rehabilitation using vision and non vision based sensors: a systematic review. Bosn J Basic Med Sci 12(3):193–202. https://doi.org/10.17305/bjbms.2012.2484

    Article  Google Scholar 

  53. 53.

    Gait analysis (2018) (online). https://en.wikipedia.org/wiki/Gait_analysis. Accessed 20 Oct 2018

  54. 54.

    Kharb A, Saini V (2011) YK Jain and Surender Dhiman (2011) A review of gait cycle and its patameters. Int J Comput Eng Manag 13:78–83

    Google Scholar 

  55. 55.

    Perry J, Burnfield J (2010) Gait analysis normal and pathological function. Slack Incorporated, Thorofare

    Google Scholar 

  56. 56.

    Understanding normal and pathological Gait (2018) (online). http://www.orthosurgery.gr/parousiasis/basic_science/7.pdf. Accessed 8 Nov 2018

  57. 57.

    Baker R (2007) The history of gait analysis before the advent of modern computers. Gait Posture 26(3):331–342. https://doi.org/10.1016/j.gaitpost.2006.10.014

    Article  Google Scholar 

  58. 58.

    Clinical Gait analysis (2018) (online). http://www.clinicalgaitanalysis.com/. Accessed 4 Oct 2018

  59. 59.

    On the Motion of Animals. Accessed: Oct 3, 2018 (online). https://galileo.ou.edu/exhibits/motion-animals-1680-81

  60. 60.

    Weber W, Weber EF. Mechanik Der Menschlichen Gehwerkzeuge. Göttingen, Germany: Dieterich, 1836 (online). https://catalog.hathitrust.org/Record/008593866

  61. 61.

    Muybridge E (1958) The human figure in motion. Coll Art J 11(3):336–337

    Google Scholar 

  62. 62.

    Marey E (1874) Animal mechanism: a treatise on terrestrial and aerial locomotion. Henry S. King & Co., London

    Google Scholar 

  63. 63.

    Braune W, Fischer O (1988) Determination of the moments of inertia of the human body and its limbs. Springer, Berlin

    Google Scholar 

  64. 64.

    Connor P, Ross A (2018) Biometric recognition by gait: a survey of modalities and features. Comput Vis Image Underst 167:1–27. https://doi.org/10.1016/j.cviu.2018.01.007

    Article  Google Scholar 

  65. 65.

    Inman VT, Ralston H, Todd F (1981) Human walking. Williams & Wilkins, London

    Google Scholar 

  66. 66.

    Eberhart H, Inman V (1947) Fundamental studies of human locomotion and other information relating to design of artificial limbs. Rep Nat Res Council, University of California, Berkeley, CA, Technical Report 1

  67. 67.

    Murray MP, Drought AB, Kory RC (1964) Walking patterns of normal men. J Bone Joint Surg 46(2):335–360

    Article  Google Scholar 

  68. 68.

    Middlesworth M (2018) The Definition and caused of Musculoskeletal disorders (MSDs): May 15 (online). https://ergo-plus.com/musculoskeletal-disorders-msd/. Accessed 30 Nov 2018

  69. 69.

    Woolf AD, Pfleger B (2003) Burden of major musculoskeletal conditions. Bull World Health Organ 81(9):646–656. https://doi.org/10.1590/S0042-96862003000900007

    Article  Google Scholar 

  70. 70.

    Arthritis Care (2018) Understanding Arthritis (online). https://arthritiscare.org.uk/assets/000/001/429/Understanding_FINAL_100516_web_original.pdf?1463670233. Accessed 29 Nov 2018

  71. 71.

    Lancet T (2017) Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Global Health Metr 390:1211–1259. https://doi.org/10.1016/S0140-6736(17)32154-2

    Article  Google Scholar 

  72. 72.

    Apostolos Kontzias, Osteoarthritis (OA): July, 2017. (online). https://www.msdmanuals.com/home/bone,-joint,-and-muscle-disorders/joint-disorders/osteoarthritis-oa. Accessed 16 Sep2018

  73. 73.

    Arthritis: Jan 7, 2019 (online). https://www.encyclopedia.com/medicine/diseases-and-conditions/pathology/arthritis. Accessed 8 Jan 2019

  74. 74.

    Turkiewicz A et al (2014) Current and future impact of osteoarthritis on health care: a population-based study with projections to year 2032. Osteoarthr Cartil 22(11):1826–1832. https://doi.org/10.1016/j.joca.2014.07.015

    Article  Google Scholar 

  75. 75.

    SE Gabriel and KMichaud (2009) Epidemiological studies in incidence, prevalence, mortality, and comorbidity of the rheumatic diseases. Arthritis Res Ther 11(3):229. https://doi.org/10.1186/ar2669

    Article  Google Scholar 

  76. 76.

    Liikavainio T (2010) Biomechanics of gait and physical function in patients with knee osteoarthritis thigh muscle properties and joint loading assessment. Ph.D. dissertation, Dept Physics and Mathematics, Eastern Finland Univ (13), Kuopio 2010

  77. 77.

    Vincent TL, Watt FE (2018) Osteoarthritis. Medicine 46(3):187–195

    Article  Google Scholar 

  78. 78.

    Osteoarthritis of the Knee: Dec 13, 2018 (online). https://www.dovemed.com/diseases-conditions/osteoarthritis-of-the-knee/

  79. 79.

    Dr Mary Lowth, Patient: Aug 12, 2014 (online). patient.info/doctor/abnormal-gait. Accessed 25 Oct 2018

  80. 80.

    Kohn MD, Sassoon AA, Fernando ND (2016) Classifications in brief: Kellgren–Lawrence classification of osteoarthritis. Clin Orthop Relat Res 474(8):1886–1893. https://doi.org/10.1007/s11999-016-4732-4

    Article  Google Scholar 

  81. 81.

    Sutherland DH (2001) The evolution of clinical gait analysis part l: kinesiological EMG. Gait Posture 14(1):61–70. https://doi.org/10.1016/S0966-6362(01)00100-X

    Article  Google Scholar 

  82. 82.

    Sutherland DH (2002) The evolution of clinical gait analysis Part II: kinematics. Gait Posture 16(2):159–179. https://doi.org/10.1016/S0966-6362(02)00004-8

    Article  Google Scholar 

  83. 83.

    Culhane KM, O’Connor M, Lyons D, Lyons GM (2005) Accelerometers in rehabilitation medicine for older adults. Age Ageing 34(6):556–560. https://doi.org/10.1093/ageing/afi192

    Article  Google Scholar 

  84. 84.

    Motion Capture (online). https://en.wikipedia.org/wiki/Motion_capture. Accessed 10 Nov 2018

  85. 85.

    Motion Capture (online). https://www.revolvy.com/page/Motion-capture. Accessed 18 Nov 2018

  86. 86.

    Collins AT et al (2011) 180 Knee Kinematics and kinetics of gait are altered by stochastic resonance stimulation and knee sleeve in knee Osteoarthritis. Osteoarthr Cartil 19:S90. https://doi.org/10.1016/S1063-4584(11)60207-0

    Article  Google Scholar 

  87. 87.

    Liikavainio T, Bragge T, Hakkarainen M, Karjalainen PA, Arokoski JP (2010) Gait and muscle activation changes in men with knee osteoarthritis. Knee 17(1):69–76. https://doi.org/10.1016/j.knee.2009.05.003

    Article  Google Scholar 

  88. 88.

    Moustakidis SP, Theocharis JB, Giakas G (2010) A fuzzy decision tree-based SVM classifier for assessing osteoarthritis severity using ground reaction force measurements. Med Eng Phys 32(10):1145–1160. https://doi.org/10.1016/j.medengphy.2010.08.006

    Article  Google Scholar 

  89. 89.

    Mezghani N et al (2008) Automatic classification of asymptomatic and osteoarthritis knee gait patterns using kinematic data features and the nearest neighbor classifier. IEEE Trans Biom Eng 55(3):1230–1232. https://doi.org/10.1109/TBME.2007.905388

    Article  Google Scholar 

  90. 90.

    Childs JD, Sparto PJ, Kelley Fitzgerald G, Bizzini M, Irrgang JJ (2004) Alterations in lower extremity movement and muscle activation patterns in individuals with knee osteoarthritis. Clin Biomech 19(1):44–49. https://doi.org/10.1016/j.clinbiomech.2003.08.007

    Article  Google Scholar 

  91. 91.

    Chen K-H, Chen P-C, Liu K-C, Chan C-T (2015) Wearable sensor-based rehabilitation exercise assessment for knee osteoarthritis. Sensors 15(2):4193–4211. https://doi.org/10.3390/s150204193

    Article  Google Scholar 

  92. 92.

    Tereso A, Martins MM, Santos CP (2015) Evaluation of gait performance of knee osteoarthritis patients after total knee arthroplasty with different assistive devices. Res Biomed Eng 31(3):208–217. https://doi.org/10.1590/2446-4740.0729

    Article  Google Scholar 

  93. 93.

    Atallah L et al (2014) Gait asymmetry detection in older adults using a light ear-worn sensor. Physiol Meas 35(5):29–40. https://doi.org/10.1088/0967-3334/35/5/N29

    Article  Google Scholar 

  94. 94.

    McCarthy I, Hodgins D, Mor A, Elbaz A, Segal G (2013) Analysis of knee flexion characteristics and how they alter with the onset of knee osteoarthritis: a case control study. BMC Musculoskelet Disord 14:169. https://doi.org/10.1186/1471-2474-14-169

    Article  Google Scholar 

  95. 95.

    Bolink S, van Laarhoven SN, Lipperts M, Heyligers IC, Grimm B (2012) Inertial sensor motion analysis of gait, sit–stand transfers and step-up transfers: differentiating knee patients from healthy controls. Physiol Meas 33(11):1947–1958. https://doi.org/10.1088/0967-3334/33/11/1947

    Article  Google Scholar 

  96. 96.

    Alkjaer T et al (2015) Gait variability and motor control in people with knee osteoarthritis. Gait Posture 42(4):479–484. https://doi.org/10.1016/j.gaitpost.2015.07.063

    Article  Google Scholar 

  97. 97.

    Hubley-Kozey C, Deluzio K, Dunbar M (2008) Muscle co-activation patterns during walking in those with severe knee osteoarthritis. Clin Biomech 23(1):71–80. https://doi.org/10.1016/j.clinbiomech.2007.08.019

    Article  Google Scholar 

  98. 98.

    Ling SM et al (2007) Electromyographic patterns suggest changes in motor unit physiology associated with early osteoarthritis of the knee. Osteoarthr Cartil 15(10):1134–1140. https://doi.org/10.1016/j.joca.2007.03.024

    Article  Google Scholar 

  99. 99.

    Arita H et al (2016) Patient-oriented outcome meadure for knee osteoarthritis is associated with gait analysis data obtained from the novel downsized motion capture technology in patients with the end-stage knee osteoarthritis. Osteoarthr Cartil 24(1):S127. https://doi.org/10.1016/j.joca.2016.01.249

    Article  Google Scholar 

  100. 100.

    Bergmann JHM et al (2013) An attachable clothing sensor system for measuring knee joint angles. IEEE Sens J 13(10):4090–4097. https://doi.org/10.1109/jsen.2013.2277697

    Article  Google Scholar 

  101. 101.

    Kiss RM (2011) Effect of severity of knee osteoarthritis on the variability of gait parameters. J Electromyogr Kinesiol 21(5):695–703. https://doi.org/10.1016/j.jelekin.2011.07.011

    Article  Google Scholar 

  102. 102.

    Metcalfe AJ et al (2017) Abnormal loading and functional deficits are present in both limbs before and after unilateral knee arthroplasty. Gait Posture 55:109–115. https://doi.org/10.1016/j.gaitpost.2017.04.008

    Article  Google Scholar 

  103. 103.

    Sun J et al (2017) Clinical gait evaluation of patients with knee osteoarthritis. Gait Posture 58:319–324. https://doi.org/10.1016/j.gaitpost.2017.08.009

    Article  Google Scholar 

  104. 104.

    Matsumoto H et al (2015) Diagnosis of knee osteoarthritis and gait variability increases risk of falling for osteoporotic older adults: the GAINA study. Osteoporosis Sarcopenia 1(1):46–52. https://doi.org/10.1016/j.afos.2015.07.003

    Article  Google Scholar 

  105. 105.

    Henriksen M, Aaboe J, Bliddal H (2012) The relationship between pain and dynamic knee joint loading in knee osteoarthritis varies with radiographic disease severity: a cross sectional study. Knee 19(4):392–398. https://doi.org/10.1016/j.knee.2011.07.003

    Article  Google Scholar 

  106. 106.

    Koktas NS, Yalabik N, Yavuzer G, Duin RPW (2010) A multi-classifier for grading knee osteroarthritis using gait analysis. Pattern Recogn Lett 31(9):898–904. https://doi.org/10.1016/j.patrec.2010.01.003

    Article  Google Scholar 

  107. 107.

    Koktas NS, Yalabik N, Yavuzer G (2006) Ensemble classifiers for medical diagnosis of knee osteoarthritis using gait data. In: 5th international conference on machine learning and applications, IEEE, USA. https://doi.org/10.1109/icmla.2006.22

  108. 108.

    Surer E, Kose A (2011) Methods and technologies for gait analysis. Computer Analysis of Human Behavior, Springer, New York, Ch. 5. https://doi.org/10.1007/978-0-85729-994-9_5

  109. 109.

    Middleton L, Buss AA, Bazin AI, Nixon MS (2005) A floor sensor system for gait recognition. In: Fourth IEEE workshop on automatic identification advanced technologies (AutoID’05), USA, pp 171–176. https://doi.org/10.1109/autoid.2005.2

  110. 110.

    Md Akhtaruzzaman A, Shafie A, Raisuudin Khan M (2016) Gait analysis: systems, technologies, and importance. J Mech Med Biol 16(7):1630003. https://doi.org/10.1142/s0219519416300039

    Article  Google Scholar 

  111. 111.

    Ko S-U, Ling SM, Schreiber C, Nesbitt M, Ferrucci L (2010) Gait patterns during different walking conditions in older adults with and without knee osteoarthritis—results from the Baltimore longitudinal study of aging. Gait Posture 33(2):205–210. https://doi.org/10.1016/j.gaitpost.2010.11.006

    Article  Google Scholar 

  112. 112.

    Henriksen M, Graven-Nielsen T, Aaboe J, Andriacchi TP, Bliddal H (2010) Gait changes in patients with knee osteoarthritis are replicated by experimental knee pain. Arthr Care Res 62(4):501–509. https://doi.org/10.1002/acr.20033

    Article  Google Scholar 

  113. 113.

    Bejek Z, Paroczai R, Illyes A, Kiss RM (2006) The influence of walking speed on gait parameters in healthy people and in patients with osteoarthritis. Knee Surg Sports Traumatol Arthrosc 14(7):612–622. https://doi.org/10.1007/s00167-005-0005-6

    Article  Google Scholar 

  114. 114.

    Serkan TAS et al (2014) Effects of severity of osteoarthritis on the temporospatial gait parameters in patients with knee osteoarthritis. Acta Orthop Traumatol Turc 48(6):635–641. https://doi.org/10.3944/AOTT.2014.13.0071

    Article  Google Scholar 

  115. 115.

    Rutherford D, Baker M, Wong I, Stanish W (2017) The effect of age and knee osteoarthritis on muscle activation patterns and knee joint biomechanics during dual belt treadmill gait. J Electromyogr Kinesiol 34:58–64. https://doi.org/10.1016/j.jelekin.2017.04.001

    Article  Google Scholar 

  116. 116.

    Chehab EF, Favre J, Erhart-Hledik JC, Andriacchi TP (2014) Baseline knee adduction and flexion moments during walking are both associated with 5 year cartilage changes in patients with medial knee osteoarthritis. Osteoarthr Cartil 22(11):1833–1839. https://doi.org/10.1016/j.joca.2014.08.009

    Article  Google Scholar 

  117. 117.

    Calder KM et al (2014) Knee power is an important parameter in understanding medial knee joint load in knee osteoarthritis. Arthritis Care Res 66(5):687–694. https://doi.org/10.1002/acr.22223

    Article  Google Scholar 

  118. 118.

    Meyera AJ et al (2013) Are external knee load and EMG measures accurate indicators of internal knee contact forces during gait? J Orthop Res 31(6):921–929. https://doi.org/10.1002/jor.22304

    Article  Google Scholar 

  119. 119.

    O’Connell M, Farrokhi S, Gil AB, Fitzgerald GK (2013) Severity of coexisting patellofemoral osteoarthritis is associated with altered sagittal-plane gait biomechanics in patients with tibiofemoral osteoarthritis. Osteoarthr Cartil 21:S87. https://doi.org/10.1016/j.joca.2013.02.187

    Article  Google Scholar 

  120. 120.

    Zeni JA Jr, Higginson JS (2009) Differences in gait parameters between healthy subjects and persons with moderate and severe knee osteoarthritis: a result of altered walking speed? Clin Biomech 24(4):372–378. https://doi.org/10.1016/j.clinbiomech.2009.02.001

    Article  Google Scholar 

  121. 121.

    Walker CRC, Myles C, Nutton R, Rowe P (2001) Movement of the knee in osteoarthritis: the use of electrogonimetry to assess function. Bone Joint J 83(2):195–198

    Article  Google Scholar 

  122. 122.

    Hurwitz DE et al (2000) Knee pain and joint loading in subjects with osteoarthritis of the knee. J Orthop Res 18(4):572–579. https://doi.org/10.1002/jor.1100180409

    Article  Google Scholar 

  123. 123.

    Monil K, Milad M, Lynsey D, Margarita K, Alison M (2018) Comparison of gait biomechanics in patients with and without knee osteoarthritis during different phases of gait. J Orthop Trauma Rehabil 25:11–15. https://doi.org/10.1016/j.jotr.2017.09.005

    Article  Google Scholar 

  124. 124.

    Na A, Piva SR, Buchanan TS (2018) Influences of knee osteoarthritis and walking difficulty on knee kinematics and kinetics. Gait Posture 61:439–444. https://doi.org/10.1016/j.gaitpost.2018.01.025

    Article  Google Scholar 

  125. 125.

    Paterson KL et al (2017) The influence of sex and obesity on gait biomechanics in people with severe knee osteoarthritis scheduled for arthroplasty. Clin Biomech 49:72–77. https://doi.org/10.1016/j.clinbiomech.2017.08.013

    Article  Google Scholar 

  126. 126.

    Preece SJ, Jones RK, Brown CA, Cacciatore TW, Jones Anthony K P (2016) Reductions in co-contraction following neuromuscular re-education in people with knee osteoarthritis. BMC Musculoskelet Disord 17:372. https://doi.org/10.1186/s12891-016-1209-2

    Article  Google Scholar 

  127. 127.

    Chang AH et al (2015) External knee adduction and flexion moments during gait and medial tibiofemoral disease progression in knee osteoarthritis. Osteoarthr Cartil 23(7):1099–1106. https://doi.org/10.1016/j.joca.2015.02.005

    Article  Google Scholar 

  128. 128.

    Duffell LD, Southgate DFL, Gulati V, McGregor AH (2014) Balance and gait adaptations in patients with early knee osteoarthritis. Gait Posture 39(4):1057–1061. https://doi.org/10.1016/j.gaitpost.2014.01.005

    Article  Google Scholar 

  129. 129.

    Farrokhi S et al (2013) Severity of coexisting patellofemoral disease is associated with increased impairments and functional limitations in patients with knee osteoarthritis. Arthr Care Res 65(4):544–551. https://doi.org/10.1002/acr.21866

    MathSciNet  Article  Google Scholar 

  130. 130.

    Simic M, Wrigley TV, Hinman RS, Hunt MA, Bennel KL (2013) Altering foot progression angle in people with medial knee osteoarthritis: the effects of varying toe-in and toe-out angles are mediated by pain and malalignment. Osteoarthr Cartil 21(9):1272–1280. https://doi.org/10.1016/j.joca.2013.06.001

    Article  Google Scholar 

  131. 131.

    Hubley-Kozey CL, Robbins SM, Rutherford DJ, Stanish WD (2013) Reliability of surface electromyographic recordings during walking in individuals with knee osteoarthritis. J Electromyogr Kinesiol 23(2):334–341. https://doi.org/10.1016/j.jelekin.2012.12.002

    Article  Google Scholar 

  132. 132.

    Kumar D, Rudolph KS, Manal KT (2012) An EMG-driven modeling approach to muscle force and joint load estimations: case study in knee osteoarthritis. J Orthop Res 30(3):377–383. https://doi.org/10.1002/jor.21544

    Article  Google Scholar 

  133. 133.

    Esquenazi A, Talaty M (2015) Gait analysis: technology and clinical applications. Phys Med Rehabil 2015, Ch. 5

  134. 134.

    Falconer J, Hayes KW (1991) A simple method to measure gait for use in arthritis clinical research. Arthritis Care Res 4(1):52–57. https://doi.org/10.1002/art.1790040110

    Article  Google Scholar 

  135. 135.

    Sacco ICN et al (2012) Joint loading decreased by inexpensive and minimalist footwear in elderly women with knee osteoarthritis during stair descent. Arthritis Care Res 64(3):368–374. https://doi.org/10.1002/acr.20690

    Article  Google Scholar 

  136. 136.

    Stephanie (2014) Statistics how to: Jan 20, 2014 (online). https://www.statisticshowto.datasciencecentral.com/parametric-and-non-parametric-data/. Accessed 23 Nov 2018

  137. 137.

    Astephen JL, Deluzio KJ, Caldwell GE, Dunbar MJ (2012) Biomechanical changes at the hip, knee, and ankle joints during gait are associated with knee osteoarthritis severity. J Orthop Res 26(3):377–383. https://doi.org/10.1002/jor.20496

    Article  Google Scholar 

  138. 138.

    Hall M et al (2017) The knee adduction moment and knee osteoarthritis symptoms: relationships according to radiographic disease severity. Osteoarthr Cartil 25(1):34–41. https://doi.org/10.1016/j.joca.2016.08.014

    Article  Google Scholar 

  139. 139.

    Paterson KL, Hinman RS, Metcalf BR, Bennell KL, Wrigley TV (2017) Plug-in-Gait calculation of the knee adduction moment in people with knee osteoarthritis during shod walking: comparison of two different foot marker models. J Foot Ankle Res 10(1):8. https://doi.org/10.1186/s13047-017-0187-4

    Article  Google Scholar 

  140. 140.

    Rutherford D, Baker M, Wong I, Stanish W (2017) The effect of age and knee osteoarthritis on muscle activation patterns and knee joint biomechanics during dual belt treadmill gait. J Electromyogr Kinesiol 34:58–64. https://doi.org/10.1016/j.jelekin.2017.04.001

    Article  Google Scholar 

  141. 141.

    Khandha A et al (2017) Gait mechanics in those with/without medial compartment knee osteoarthritis 5 years after anterior cruciate ligament reconstruction. J Orthop Res 35(3):625–633. https://doi.org/10.1002/jor.23261

    Article  Google Scholar 

  142. 142.

    MacLean KFE, Callaghan JP, Maly MR (2016) Effect of obesity on knee joint biomechanics during gait in young adults. Cogent Med 3(1):1173778. https://doi.org/10.1080/2331205X.2016.1173778

    Article  Google Scholar 

  143. 143.

    Karatsidis A et al (2017) Estimation of ground reaction forces and moments during gait using only inertial motion capture. Sensors 17(1):75. https://doi.org/10.3390/s17010075

    Article  Google Scholar 

  144. 144.

    Sparling TL et al (2014) Energy recovery in individuals with knee osteoarthritis. Osteoarthr Cartil 22(6):747–755. https://doi.org/10.1016/j.joca.2014.04.004

    MathSciNet  Article  Google Scholar 

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  1. School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, 182320, India

    Navleen Kour, Sunanda Gupta & Sakshi Arora

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Figure 5: http://www.clinicalgaitanalysis.com/https://en.wikipedia.org/wiki/Aristotlehttps://www.leonardodavinci.net/https://www.onthisday.com/people/girolamo-cardanohttps://www.historyonthenet.com/galileo-galilei/https://www.biography.com/people/ren-descartes-37613https://commons.wikimedia.org/wiki/File:Portrait_of_Giovanni_Alfonso_Borelli_Wellcome_L0010325.jpghttps://thomaspjohnston.wordpress.com/category/postage-stamps/page/2/https://www.thefamouspeople.com/profiles/hermann-von-helmholtz-4828.phphttps://en.wikipedia.org/wiki/Eadweard_Muybridgehttps://sheridanphothistory.wordpress.com/page/2/https://me.queensu.ca/People/Deluzio/JAM/files/Baker.pdfhttps://me.queensu.ca/People/Deluzio/JAM/files/Baker.pdfhttps://ouhsc.edu/bserdac/dthompso/web/gait/knmatics/saunders.htmhttps://www.nap.edu/read/4779/chapter/14http://www.clinicalgaitanalysis.com/history/modern.htmlhttps://www.legacy.com/obituaries/sfgate/obituary.aspx?n=david-h-sutherland&pid=17877856https://ptceu.wordpress.com/2013/05/01/in-celebration-of-my-friend-and-mentor-dr-jacquelin-perry/.

Figure 7: https://www.dovemed.com/diseases-conditions/osteoarthritis-of-the-knee/ Figure 8: https://me.queensu.ca/People/Deluzio/JAM/files/Baker.pdfhttps://www.nap.edu/read/4779/chapter/14https://ouhsc.edu/bserdac/dthompso/web/gait/knmatics/saunders.htmhttps://en.wikipedia.org/wiki/Robert_H._Goddardhttp://www.oemupdate.com/feature/kistler-group-acquires-ios-gmbh/http://www.clinicalgaitanalysis.com/history/modern.htmlhttps://www.digitalcommonwealth.org/search/commonwealth-oai:73666c049https://ar-conf.ru/en/news/pochuvstvuyte-virtualnie-obyatya-tesla-suit-na-ar-conference-30543https://news.creativecow.net/company.php?folder=Viconhttps://www.mediaproductionshow.com/exhibitors/ikegami/https://www.dualshockers.com/microsoft-discontinuing-original-kinect-models-for-windows-pc-in-2015/https://www.telegraph.co.uk/education/universityeducation/8742154/Top-UK-universities-in-world-rankings.htmlhttp://physics.kenyon.edu/EarlyApparatus/Static_Electricity/Geissler_Tubes/Geissler_Tubes.htmlhttps://www.mdpi.com/1424-8220/14/2/3362/htmlhttps://www.researchgate.net/publication/301935875_Human_Gait_and_Clinical_Movement_Analysishttp://dynamolab.umed.pl/?page_id=34&lang=enhttps://www.amazon.co.uk/Tobar-01544-Gyroscope-Silver-10cm/dp/B000H6W52Shttps://www.sparkfun.com/products/retired/692https://www.amti.biz/fps-guide.aspxhttp://www.bleng.com/traditional-reflective-markershttps://www.uline.ca/Product/Detail/S-17177/Vinyl-Safety-Reflective-Tapes/Reflective-Tape-3-x-10-yds Red http://www.taheeltech.com/product/goniometers/http://www.phasespace.com/suits.htmlhttps://vizworld.com/2012/08/vicon-showcase-motion-capture-products-siggraph2012/https://www.amazon.in/Camcorders-Camcorder-Digital-External-Microphone/dp/B076DXMXMRhttps://www.dhgate.com/product/2017-anti-blue-light-screen-protector-for/402728044.htmlhttps://www.generationrobots.com/en/401430-microsoft-kinect-sensor.htmlhttps://www.microsoft.com/en-us/research/project/kinect-for-windows-sdk-beta/https://www.ordineinfermieribologna.it/2016/il-futuro-con-gli-wearable-ecco-la-maglietta-che-rileva-il-ritmo-cardiaco-e-lo-trasmette.html.

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Kour, N., Gupta, S. & Arora, S. A Survey of Knee Osteoarthritis Assessment Based on Gait. Arch Computat Methods Eng 28, 345–385 (2021). https://doi.org/10.1007/s11831-019-09379-z

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