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Concurrent validation of inertial sensors for measurement of knee kinematics in individuals with knee osteoarthritis: A technical report

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Abstract

The validity of inertial sensor-based kinematic measurements in individuals with knee osteoarthritis has not previously been investigated. This study assessed the concurrent validity of inertial sensors in measuring knee kinematics in individuals with knee osteoarthritis when compared to the Vicon motion analysis system, and explored the difference between two calculation methods (traditional approach versus functional approach). Nineteen participants with knee osteoarthritis performed functional tasks with DorsaVi sensors (fixed with Vicon markers) worn on thighs and shanks. Peak and time-series knee flexion and extension (for both DorsaVi and Vicon systems) were calculated using the two calculation methods. Agreement between the systems was estimated by calculating root mean squared errors, mean differences and 95% limits of agreement. For the traditional approach, the root mean squared error between the DorsaVi and Vicon measurements ranged from 1.70°-3.02° for peak and 3.72–4.67° for time-series knee flexion and extension. For the functional approach, the root mean squared error ranged from 1.77°-3.18° for peak and 4.58°-5.04° for time-series knee flexion and extension. The mean difference varied across tasks (traditional approach: -0.16°-3.76°, functional approach: 0.96°-4.94°), and the limits of agreement showed high variability between the DorsaVi and Vicon measurements across the sample. Although there appears to be acceptable agreement between the systems for measuring knee kinematics, there was high variability in measurement differences across the dataset. In addition, a functional calibration approach does not appear to improve the accuracy of inertial sensor-based knee kinematics.

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References

  1. Cui A, et al. Global, regional prevalence, incidence and risk factors of knee osteoarthritis in population-based studies. E Clinic. Med. 2020;29–30

  2. Vos T, et al. 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. The Lancet. 2012;380(9859):2163–96.

    Article  Google Scholar 

  3. Heidari B. Knee osteoarthritis prevalence, risk factors, pathogenesis and features: Part I. Caspian J Intern Med. 2011;2(2):205–12.

    Google Scholar 

  4. van der Straaten R, et al. Mobile assessment of the lower limb kinematics in healthy persons and in persons with degenerative knee disorders: A systematic review. Gait Posture. 2018;59:229–41.

    Article  Google Scholar 

  5. Grimm B, Bolink S. Evaluating physical function and activity in the elderly patient using wearable motion sensors. EFORT Open Rev. 2016;1(5):112–20.

    Article  Google Scholar 

  6. Calliess T, et al. Clinical evaluation of a mobile sensor-based gait analysis method for outcome measurement after knee arthroplasty. Sensors (Basel). 2014;14(9):15953–64.

    Article  Google Scholar 

  7. Astephen J, Deluzio K. 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. 2005;20(2):209–17.

    Article  Google Scholar 

  8. Kaufman KR, et al. Gait characteristics of patients with knee osteoarthritis. J Biomech. 2001;34(7):907–15.

    Article  Google Scholar 

  9. Chen CP, et al. Sagittal plane loading response during gait in different age groups and in people with knee osteoarthritis. Am J Phys Med Rehabil. 2003;82(4):307–12.

    Google Scholar 

  10. Baliunas AJ, et al. Increased knee joint loads during walking are present in subjects with knee osteoarthritis. Osteoarthritis Cartilage. 2002;10(7):573–9.

    Article  Google Scholar 

  11. Smith AJ, Lloyd DG, Wood DJ. Pre-surgery knee joint loading patterns during walking predict the presence and severity of anterior knee pain after total knee arthroplasty. J Orthop Res. 2004;22(2):260–6.

    Article  Google Scholar 

  12. Al-Zahrani KS, Bakheit AMO. A study of the gait characteristics of patients with chronic osteoarthritis of the knee. Disabil Rehabil. 2009;24(5):275–80.

    Article  Google Scholar 

  13. Gök H, Ergin S, Yavuzer G. Kinetic and kinematic characteristics of gait in patients with medial knee arthrosis. Acta Orthop Scand. 2011;73(6):647–52.

    Article  Google Scholar 

  14. Heiden TL, Lloyd DG, Ackland TR. Knee joint kinematics, kinetics and muscle co-contraction in knee osteoarthritis patient gait. Clin Biomech. 2009;24(10):833–41.

    Article  Google Scholar 

  15. Childs JD, et al. Alterations in lower extremity movement and muscle activation patterns in individuals with knee osteoarthritis. Clin Biomech. 2004;19(1):44–9.

    Article  Google Scholar 

  16. Vlaeyen JW, Linton SJ. Fear-avoidance model of chronic musculoskeletal pain: 12 years on. Pain. 2012;153(6):1144–7.

    Article  Google Scholar 

  17. Vlaeyen JW, Linton SJ. Fear-avoidance and its consequences in chronic musculoskeletal pain: a state of the art. Pain. 2000;85(3):317–32.

    Article  Google Scholar 

  18. Picerno P. 25 years of lower limb joint kinematics by using inertial and magnetic sensors: A review of methodological approaches. Gait Posture. 2017;51:239–46.

    Article  Google Scholar 

  19. Grood ES, Suntay WJ. A Joint Coordinate System for the Clinical Description of Three-Dimensional Motions: Application to the Knee. J Biomech Eng. 1983;105(2):136–44.

    Article  Google Scholar 

  20. Wu G, et al. 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;35:543–8.

    Article  Google Scholar 

  21. Wu G, et al. ISB recommendations 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;38:981–92.

    Article  Google Scholar 

  22. Seel T, Raisch J, Schauer T. IMU-based joint angle measurement for gait analysis. Sensors (Basel). 2014;14(4):6891–909.

    Article  Google Scholar 

  23. Favre J, et al. Functional calibration procedure for 3D knee joint angle description using inertial sensors. J Biomech. 2009;42(14):2330–5.

    Article  Google Scholar 

  24. Cuesta-Vargas AI, Galan-Mercant A, Williams JM. The use of inertial sensors system for human motion analysis. Phys Ther Rev. 2010;15(6):462–73.

    Article  Google Scholar 

  25. Poitras I, et al, Validity and Reliability of Wearable Sensors for Joint Angle Estimation: A Systematic Review. Sensors (Basel, 2019;19(7)

  26. Al-Amri M, et al. Inertial Measurement Units for Clinical Movement Analysis: Reliability and Concurrent Validity. Sensors (Basel), 2018;18(3)

  27. Zhang JT, et al. Concurrent validation of Xsens MVN measurement of lower limb joint angular kinematics. Physiol Meas. 2013;34(8):N63–9.

    Article  Google Scholar 

  28. Mjosund HL, et al. Clinically acceptable agreement between the ViMove wireless motion sensor system and the Vicon motion capture system when measuring lumbar region inclination motion in the sagittal and coronal planes. BMC Musculoskelet Disord. 2017;18(1):124.

    Article  Google Scholar 

  29. Drapeaux A, Carlson K. A Comparison of Inertial Motion Capture Systems: DorsaVi and Xsens. Internatl J Kinesiol Sports Sci. 2020;8(3)

  30. Hinman RS, et al. Acupuncture for chronic knee pain: a randomized clinical trial. JAMA. 2014;312(13):1313–22.

    Article  Google Scholar 

  31. Kulkarni K, et al. Obesity and osteoarthritis. Maturitas. 2016;89:22–8.

    Article  Google Scholar 

  32. Thijssen E, van Caam A, van der Kraan PM. Obesity and osteoarthritis, more than just wear and tear: pivotal roles for inflamed adipose tissue and dyslipidaemia in obesity-induced osteoarthritis. Rheumatology (Oxford). 2015;54(4):588–600.

    Article  Google Scholar 

  33. Blagojevic M, et al. Risk factors for onset of osteoarthritis of the knee in older adults: a systematic review and meta-analysis. Osteoarthritis Cartilage. 2010;18(1):24–33.

    Article  Google Scholar 

  34. National Institute for Health and Care Excellence. Osteoarthritis: care and management (clinical guideline CG177). 2014 [cited 2021 1 March]; Available from: https://www.nice.org.uk/guidance/cg177/resources/osteoarthritis-care-and-management-pdf-35109757272517

  35. Roos EM, et al. Knee injury and osteoarthritis outcome score (KOOS) - Development of a self-administered outcome measure. J Orthop Sports Phys Ther. 1998;28(2):88–96.

    Article  Google Scholar 

  36. Xsens Technologies B.V. MVN User Manual: User Guide MVN, MVN BIOMECH MVN Link, MVN Awinda. [accessed on 29 January 2021]; Available from: https://fccid.io/QILMTW2-3A7G6/User-Manual/Users-Manual-2695756

  37. Roetenberg D, Luinge H, Slycke P. Xsens MVN: Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors. Xsens Motion Technologies BV, Tech Rep 2009

  38. Besier TF, et al. Repeatability of gait data using a functional hip joint centre and a mean helical knee axis. J Biomech. 2003;36:1159–68.

    Article  Google Scholar 

  39. Cutti AG, et al. ‘Outwalk’: a protocol for clinical gait analysis based on inertial and magnetic sensors. Med Biol Eng Compu. 2010;48(1):17.

    Article  Google Scholar 

  40. Bolink SA, et al. Validity of an inertial measurement unit to assess pelvic orientation angles during gait, sit-stand transfers and step-up transfers: Comparison with an optoelectronic motion capture system. Med Eng Phys. 2016;38(3):225–31.

    Article  Google Scholar 

  41. Bland JaAD, Bland and Altman (2007) - Agreement.pdf. J Biopharm Stat. 2007;17(4):571–582

  42. Kok M, Schon TB. Magnetometer Calibration Using Inertial Sensors. IEEE Sens J. 2016;16(14):5679–89.

    Article  Google Scholar 

  43. Vitali RV, McGinnis RS, Perkins NC. Robust Error-State Kalman Filter for Estimating IMU Orientation. IEEE Sensors J. 2020;1–1

  44. Kalman RE. A New Approach to Linear Filtering and Prediction Problems. J Basic Eng. 1960;82(1):35–45.

    Article  MathSciNet  Google Scholar 

  45. Findlow A, et al. Predicting lower limb joint kinematics using wearable motion sensors. Gait Posture. 2008;28(1):120–6.

    Article  Google Scholar 

  46. Gholami M, Napier C, Menon C. Estimating Lower Extremity Running Gait Kinematics with a Single Accelerometer: A Deep Learning Approach. Sensors (Basel), 2020;20(10)

  47. Argent R, et al. Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor. J Rehabil Assist Technol Eng. 2019;6:2055668319868544.

    Google Scholar 

  48. Lim H, Kim B, Park S. Prediction of Lower Limb Kinetics and Kinematics during Walking by a Single IMU on the Lower Back Using Machine Learning. Sensors (Basel) 2019;20(1)

  49. Goulermas JY, et al. Regression Techniques for the Prediction of Lower Limb Kinematics. J Biomech Eng. 2005;127(6):1020–4.

    Article  Google Scholar 

  50. Rapp E, et al. Estimation of kinematics from inertial measurement units using a combined deep learning and optimization framework. J Biomech, 2021;116:110229

  51. Mundt M, et al. Prediction of lower limb joint angles and moments during gait using artificial neural networks. Med Biol Eng Comput. 2020;58(1):211–25.

    Article  Google Scholar 

  52. 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

  53. Takeda R, et al. Gait posture estimation using wearable acceleration and gyro sensors. J Biomech. 2009;42(15):2486–94.

    Article  Google Scholar 

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Funding

No funding was received for conducting this study.

Author information

Authors and Affiliations

  1. School of Allied Health, Curtin University, Kent St, Bentley, WA, 6102, Australia

    Tara Binnie, Anne Smith, Peter Kent, Leo Ng, Peter O’Sullivan, Jay-Shian Tan, Paul C. Davey & Amity Campbell

Authors
  1. Tara Binnie
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  2. Anne Smith
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  3. Peter Kent
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  4. Leo Ng
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  5. Peter O’Sullivan
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  6. Jay-Shian Tan
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  7. Paul C. Davey
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  8. Amity Campbell
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Contributions

Tara Binnie: Conceptualisation, Methodology, Formal Analysis, Investigation, Data Curation, Writing – Original Draft, Writing – Review and Editing. Anne Smith: Conceptualisation, Methodology, Formal Analysis, Writing – Review and Editing, Supervision. Peter Kent: Conceptualisation, Methodology, Formal Analysis, Writing – Review and Editing, Supervision. Leo Ng: Conceptualisation, Writing – Review and Editing. Peter O’Sullivan: Conceptualisation, Writing – Review and Editing. Jay-Shian Tan: Investigation, Data Curation, Writing – Review and Editing. Paul Davey: Methodology, Software, Data Curation, Writing – Review and Editing. Amity Campbell: Conceptualisation, Methodology, Writing – Review and Editing, Supervision.

Corresponding author

Correspondence to Tara Binnie.

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Ethics approval

The study was approved by the Curtin University Human Research Ethics Committee (HRE2017-0695) and was conducted in accordance with the NHMRC National Statement on Ethical Conduct in Research Involving Humans.

Consent to participate

All participants were provided with a participant information sheet and were required to provide written informed consent.

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Not applicable.

Conflicts of interest

The DorsaVi company provided training, technical support and the equipment at a reduced price. The company was not involved in the study design, data collection, data handling or data analysis and did not influence the reporting of the results or conclusions reached in this study. A decade ago, Peter Kent received a market-rate consulting fee from DorsaVi for advice on clinical trial design. No member of the project team has any other direct or indirect financial link to the DorsaVi company.

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Binnie, T., Smith, A., Kent, P. et al. Concurrent validation of inertial sensors for measurement of knee kinematics in individuals with knee osteoarthritis: A technical report. Health Technol. 12, 107–116 (2022). https://doi.org/10.1007/s12553-021-00616-9

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  • DOI: https://doi.org/10.1007/s12553-021-00616-9

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