SPIRA: an automatic system to support lower limb injury assessment


Lower limb injuries, especially those related to the knee joint, are some of the most common and severe injuries among sport practitioners. Consequently, a growing interest in the identification of subjects with high risk of injury has emerged during last years. One of the most commonly used injury risk factor is the measurement of joint angles during the execution of dynamic movements. To that end, techniques such as human motion capture and video analysis have been widely used. However, traditional procedures to measure joint angles present certain limitations, which makes this practice not practical in common clinical settings. This work presents SPIRA, a novel 2D video analysis system directed to support practitioners during the evaluation of joint angles in functional tests. The system employs an infrared camera to track retro-reflective markers attached to the patient’s body joints and provide a real-time measurement of the joint angles in a cost-and-time-effective way. The information gathered by the sensor is processed and managed through a computer application that guides the expert during the execution of the tests and expedites the analysis of the results. In order to show the potential of the SPIRA system, a case study has been conducted, performing the analysis with the both the proposed system and a gold-standard in 2D offline video analysis. The results (ICC(\(\rho\)) = 0.996) reveal a good agreement between both tools and prove the reliability of SPIRA.

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  1. Acevedo R, Rivera-Vega A, Miranda G, Micheo W (2014) Anterior cruciate ligament injury: identification of risk factors and prevention strategies. Curr Sports Med Rep 13(3):186–191. https://doi.org/10.1249/JSR.0000000000000053

    Article  Google Scholar 

  2. Bailon C, Damas M, Pomares H, Banos O (2017) Automatic 2D motion capture system for joint angle measurement. Springer International Publishing, Cham, pp 71–81. https://doi.org/10.1007/978-3-319-59147-6_7

  3. Banos O, Moral-Munoz J, Diaz-Reyes I, Arroyo-Morales M, Damas M, Herrera-Viedma E, Hong C, Lee S, Pomares H, Rojas I, Villalonga C (2015) mdurance: a novel mobile health system to support trunk endurance assessment. Sensors 15(6):13159–13183. https://doi.org/10.3390/s150613159

    Article  Google Scholar 

  4. Bell AL, Brand RA, Pedersen DR (1989) Prediction of hip joint centre location from external landmarks. Human Mov Sci 8(1):3–16. https://doi.org/10.1016/0167-9457(89)90020-1

    Article  Google Scholar 

  5. Bland J, Altman D (1990) A note on the use of the intraclass correlation coefficient in the evaluation of agreement between two methods of measurement. Comput Biol Med 20(5):337–340. https://doi.org/10.1016/0010-4825(90)90013-F

    Article  Google Scholar 

  6. Booth M, Orr R (2017) Time-loss injuries in sub-elite and emerging Rugby league players. J Sports Sci Med 16:295–301

    Google Scholar 

  7. Bort-Roig J, Gilson ND, Puig-Ribera A, Contreras RS, Trost SG (2014) Measuring and influencing physical activity with smartphone technology: a systematic review. Sports Med 44(5):671–686. https://doi.org/10.1007/s40279-014-0142-5

    Article  Google Scholar 

  8. Bouwmans T, Porikli F, Höferlin B, Vacavant A (2015) Background modeling and foreground detection for video surveillance, 1st edn. CRC Press/Taylor and Francis Group, oCLC: 934765046

  9. Damsted C, Larsen LH, Nielsen RO (2015) Reliability of video-based identification of footstrike pattern and video time frame at initial contact in recreational runners. Gait Posture 42(1):32–35. https://doi.org/10.1016/j.gaitpost.2015.01.029

    Article  Google Scholar 

  10. Davoody N, Hagglund M (2016) Care professionals’ perceived usefulness of ehealth for post-discharge stroke patients. Stud Health Technol Inf 228:589–593. https://doi.org/10.3233/978-1-61499-678-1-589

    Article  Google Scholar 

  11. DiCesare CA, Bates NA, Meyer GD, Hewett TE (2014) The validity of 2-dimensional measurement of trunk angle during dynamic tasks. Int J Sports Phys Ther 9(4):420–427

    Google Scholar 

  12. EmguCV (2017) EmguCV library. http://www.emgu.com/wiki/index.php/. Accessed 09 Oct 2017

  13. Feller J, Webster KE (2013) Return to sport following anterior cruciate ligament reconstruction. International Orthopaedics 37(2):285–290. https://doi.org/10.1007/s00264-012-1690-7

    Article  Google Scholar 

  14. van Gent RN, Siem D, van Middelkoop M, van Os AG, Bierma-Zeinstra SMA, Koes BW (2007) Incidence and determinants of lower extremity running injuries in long distance runners: a systematic review. Br J Sports Med 41(8):469–480. https://doi.org/10.1136/bjsm.2006.033548

    Article  Google Scholar 

  15. Guerra-Filho GB (2005) Optical motion capture: theory and implementation. J Theor Appl Inf (RITA) 12(2):61–89

    Google Scholar 

  16. Herrington L (2014) Knee valgus angle during single leg squat and landing in patellofemoral pain patients and controls. KNEE 21(2):514–517. https://doi.org/10.1016/j.knee.2013.11.011

    Article  Google Scholar 

  17. Herrington L, Munro A (2010) Drop jump landing knee valgus angle: normative data in a physically active population. Phys Ther Sport 11(2):56–59. https://doi.org/10.1016/j.ptsp.2009.11.004

    Article  Google Scholar 

  18. Hewett TE, Myer GD (2011) The mechanistic connection between the trunk, hip, knee, and anterior cruciate ligament injury. Exerc Sport Sci Rev 39(4):161–166. https://doi.org/10.1097/JES.0b013e3182297439

    Article  Google Scholar 

  19. Hewett TE, Myer GD, Ford KR, Heidt Robert SJ, Colosimo AJ, McLean SG, van den Bogert AJ, Paterno MV, Succop P (2005) Biomechanical measures of neuromuscular control and valgus loading of the knee predict anterior cruciate ligament injury risk in female athletes: a prospective study. Am J Sports Med 33(4):492–501. https://doi.org/10.1177/0363546504269591

  20. Hewett TE, Di Stasi SL, Myer GD (2013) Current concepts for injury prevention in athletes after anterior cruciate ligament reconstruction. Am J Sports Med 41(1):216–224. https://doi.org/10.1177/0363546512459638

    Article  Google Scholar 

  21. Hootman JM, Dick R, Agel J (2007) Epidemiology of collegiate injuries for 15 sports: summary and recommendations for injury prevention initiatives. J Athl Train 42(2):311–319

    Google Scholar 

  22. Kato S, Urabe Y, Kawamura K (2008) Alignment control exercise changes lower extremity movement during stop movements in female basketball players. Knee 15(4):299–304. https://doi.org/10.1016/j.knee.2008.04.003

    Article  Google Scholar 

  23. Kinovea Association (2017) Kinovea. http://www.kinovea.org/. Accessed 09 Oct 2017

  24. Li L (2014) Time-of-flight camera—an introduction. Technical White Paper SLOA190B, Texas Instruments

  25. LiveCharts (2017) LiveCharts library. https://lvcharts.net/. Accessed 09 Oct 2017

  26. Mather RC, Koenig L, Kocher MS, Dall TM, Gallo P, Scott DJ, Bach BR, Spindler KP (2013) Societal and economic impact of anterior cruciate ligament tears. J Bone Joint Surg Am 95(19):1751–1759. https://doi.org/10.2106/JBJS.L.01705

    Article  Google Scholar 

  27. Microsoft (2014a) Kinect API. https://msdn.microsoft.com/en-us/library/dn782033.aspx

  28. Microsoft (2014b) Kinect for windows v2. https://developer.microsoft.com/es-es/windows/kinect/develop/

  29. Microsoft (2017a) .NET Framework. https://www.microsoft.com/net

  30. Microsoft (2017b) Windows presentation foundation (WPF). https://docs.microsoft.com/en-us/dotnet/framework/wpf/

  31. Microsoft (2017c) XAML Overview. https://docs.microsoft.com/en-us/dotnet/framework/wpf/advanced/xaml-overview-wpf

  32. Moral-Muñoz JA, Esteban-Moreno B, Arroyo-Morales M, Cobo MJ, Herrera-Viedma E (2015) Agreement between face-to-face and free software video analysis for assessing hamstring flexibility in adolescents. J Strength Condition Res 29(9):2661–2665. https://doi.org/10.1519/JSC.0000000000000896

    Article  Google Scholar 

  33. Müller B, Ilg W, Giese MA, Ludolph N (2017) Validation of enhanced kinect sensor based motion capturing for gait assessment. PLoS One. https://doi.org/10.1371/journal.pone.0175813

  34. Munro AG (2013) The use of two-dimensional motion analysis and functional performance tests for assessment of knee injury risk behaviours in athletes. PhD thesis, University of Salford, Salford, UK

  35. Munro AG, Herrington L, Carolan M (2012) Reliability of 2-dimensional video assessment of frontal-plane dynamic knee valgus during common athletic screening tasks. J Sport Rehabilit 21(1):7–11

    Article  Google Scholar 

  36. Myles PS, Cui J (2007) Using the Bland–Altman method to measure agreement with repeated measures. Br J Anaesth 99(3):309–311. https://doi.org/10.1093/bja/aem214

    Article  Google Scholar 

  37. Northern Digital Inc (2017) Optotrak certus. http://www.ndigital.com/msci/products/optotrak-certus

  38. O’Connor S, McCaffrey N, Whyte EF, Moran KA (2017) Epidemiology of injury in male collegiate gaelic footballers in one season. Scand J Med Sci Sports 27(10):1136–1142. https://doi.org/10.1111/sms.12733

    Article  Google Scholar 

  39. Olugbara OO, Adetiba E, Oyewole SA (2015) Pixel intensity clustering algorithm for multilevel image segmentation. Math Probl Eng 649802:19. https://doi.org/10.1155/2015/649802

    Article  Google Scholar 

  40. Patro SGK, Sahu KK (2015) Normalization: a preprocessing stage. CoRR abs/1503.06462

  41. Piccardi M (2004) Background subtraction techniques: a review. In: Systems, man and cybernetics, 2004 IEEE international conference on, IEEE, vol 4, pp 3099–3104

  42. van Poppel D, Scholten-Peeters GGM, van Middelkoop M, Verhagen AP (2014) Prevalence, incidence and course of lower extremity injuries in runners during a 12-month follow-up period. Scand J Med Sci Sports 24(6):943–949. https://doi.org/10.1111/sms.12110

    Article  Google Scholar 

  43. Prieto L, Lamarca R, Casado A (1998) La evaluación de la fiabilidad en las observaciones clínicas: el coeficiente de correlación intraclase. Med Clin 110(4):142–145

    Google Scholar 

  44. Sarbolandi H, Lefloch D, Kolb A (2015) Kinect range sensing: structured-light versus time-of-flight kinect. Comput Vis Image Understand 139:1–20. https://doi.org/10.1016/j.cviu.2015.05.006

    Article  Google Scholar 

  45. Shapiro LG, Stockman GC (2002) Computer Vision, Prentice Hall, chap Binary Image Analysis, pp 69–73

  46. SQLite (2017) SQLite engine. http://www.sqlite.org/

  47. Stickler L, Finley M, Gulgin H (2015) Relationship between hip and core strength and frontal plane alignment during a single leg squat. Phys Ther Sport 16(1):66–71. https://doi.org/10.1016/j.ptsp.2014.05.002

    Article  Google Scholar 

  48. Szeliski R (2011) Computer vision: algorithms and applications. Texts in computer science, Springer, London Dordrecht Heidelberg New York, oCLC: 845585642

  49. Vicon Motion Systems Ltd (2002) Essential of motion capture. Tech. rep

  50. Vicon Motion Systems Ltd (2017) Vicon cameras. https://www.vicon.com

  51. Willson JD, Davis IS (2008) Utility of the frontal plane projection angle in females with patellofemoral pain. J Orthopaed Sports Phys Ther 38(10):606–615. https://doi.org/10.2519/jospt.2008.2706

    Article  Google Scholar 

  52. Willson JD, Ireland ML, Davis I (2006) Core strength and lower extremity alignment during single leg squats. Med Sci Sports Exerc 38(5):945–952. https://doi.org/10.1249/01.mss.0000218140.05074.fa

    Article  Google Scholar 

  53. Wyndow N, De Jong A, Rial K, Tucker K, Collins N, Vicenzino B, Russell T, Crossley K (2016) The relationship of foot and ankle mobility to the frontal plane projection angle in asymptomatic adults. J Foot Ankle Res 9(1):3

    Article  Google Scholar 

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This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) Projects TIN2015-71873-R and TIN2015-67020-P together with the European Fund for Regional Development (FEDER). This work has also been partially supported by the UGR Research Starting Grant 2017, the FPU Spanish Grant FPU16/04376 and the Dutch UT-CTIT project HoliBehave.

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Correspondence to Carlos Bailon.

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Bailon, C., Damas, M., Pomares, H. et al. SPIRA: an automatic system to support lower limb injury assessment. J Ambient Intell Human Comput 10, 2111–2123 (2019). https://doi.org/10.1007/s12652-018-0722-6

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  • Injury risk
  • Lower limb
  • Joint angles
  • Marker tracking
  • Computer vision
  • Kinect
  • 2D video analysis