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Estimation of Spinal Loading During Manual Materials Handling Using Inertial Motion Capture

  • Frederik Greve Larsen
  • Frederik Petri Svenningsen
  • Michael Skipper Andersen
  • Mark de Zee
  • Sebastian SkalsEmail author
Original Article
  • 94 Downloads

Abstract

Musculoskeletal models have traditionally relied on measurements of segment kinematics and ground reaction forces and moments (GRF&Ms) from marked-based motion capture and floor-mounted force plates, which are typically limited to laboratory settings. Recent advances in inertial motion capture (IMC) as well as methods for predicting GRF&Ms have enabled the acquisition of these input data in the field. Therefore, this study evaluated the concurrent validity of a novel methodology for estimating the dynamic loading of the lumbar spine during manual materials handling based on a musculoskeletal model driven exclusively using IMC data and predicted GRF&Ms. Trunk kinematics, GRF&Ms, L4–L5 joint reaction forces (JRFs) and erector spinae muscle forces from 13 subjects performing various lifting and transferring tasks were compared to a model driven by simultaneously recorded skin-marker trajectories and force plate data. Moderate to excellent correlations and relatively low magnitude differences were found for the L4–L5 axial compression, erector spinae muscle and vertical ground reaction forces during symmetrical and asymmetrical lifting, but discrepancies were also identified between the models, particularly for the trunk kinematics and L4–L5 shear forces. Based on these results, the presented methodology can be applied for estimating the relative L4–L5 axial compression forces under dynamic conditions during manual materials handling in the field.

Keywords

Musculoskeletal modelling Inertial motion capture Inverse dynamic analysis Predicted ground reaction forces and moments Manual materials handling Low back loading 

Nomenclature

GRF&Ms

Ground reaction forces and moments

IMC

Inertial motion capture

JRF

Joint reaction force

IDA

Inverse dynamic analysis

OMC

Optical motion capture

GRF

Ground reaction force

IMU

Inertial measurement unit

OMC-MGRF

Optical motion capture with measured ground reaction forces

OMC-PGRF

Optical motion capture with predicted ground reaction forces

IMC-PGRF

Inertial motion capture with predicted ground reaction forces

GRM

Ground reaction moment

%BW

Percentage of body weight

%BW*BH

Percentage of body weight times body height

RMSE

Root-mean-square error

rRMSE

Relative root-mean-square error

ICC

Intraclass correlation coefficient

LoA

Limits of agreement

Notes

Acknowledgments

This work was supported by the Independent Research Fund Denmark under Grant No. DFF-7026-00099 to Sebastian Skals.

Conflict of interest

Mark de Zee is co-founder of the company AnyBody Technology A/S that owns and sells the AnyBody Modeling System, which was used for the simulations. Mark de Zee is also a minority shareholder in the company.

Supplementary material

10439_2019_2409_MOESM1_ESM.pdf (1.7 mb)
Supplementary material 1 (PDF 1738 kb)

References

  1. 1.
    Andersen, M. S., M. Damsgaard, B. MacWilliams, and J. Rasmussen. A computationally efficient optimisation-based method for parameter identification of kinematically determinate and over-determinate biomechanical systems. Comput. Methods Biomech. Biomed. Eng. 13:171–183, 2010.CrossRefGoogle Scholar
  2. 2.
    Andersen, M. S., M. Damsgaard, and J. Rasmussen. Kinematic analysis of over-determinate biomechanical systems. Comput. Methods Biomech. Biomed. Eng. 12:371–384, 2009.CrossRefGoogle Scholar
  3. 3.
    Arshad, R., T. Zander, M. Dreischarf, and H. Schmidt. Influence of lumbar spine rhythms and intra-abdominal pressure on spinal loads and trunk muscle forces during upper body inclination. Med. Eng. Phys. 38:333–338, 2016.CrossRefGoogle Scholar
  4. 4.
    Bassani, T., E. Stucovitz, Z. Qian, M. Briguglio, and F. Galbusera. Validation of the AnyBody full body musculoskeletal model in computing lumbar spine loads at L4L5 level. J. Biomech. 58:89–96, 2017.CrossRefGoogle Scholar
  5. 5.
    Bland, M. J., and G. D. Altman. Measuring agreement in method comparison studies. Stat. Methods Med. Res. 8:135–160, 1999.CrossRefGoogle Scholar
  6. 6.
    Carbone, V., R. Fluit, P. Pellikaan, M. M. van der Krogt, D. Janssen, M. Damsgaard, L. Vigneron, T. Feilkas, H. F. J. M. Koopman, and N. Verdonschot. TLEM 2.0—a comprehensive musculoskeletal geometry dataset for subject-specific modeling of lower extremity. J. Biomech. 48:734–741, 2015.CrossRefGoogle Scholar
  7. 7.
    de Zee, M., L. Hansen, C. Wong, J. Rasmussen, and E. B. Simonsen. A generic detailed rigid-body lumbar spine model. J. Biomech. 40:1219–1227, 2007.CrossRefGoogle Scholar
  8. 8.
    Deyo, R. A., and J. N. Weinstein. Low back pain. N. Engl. J. Med. 344:363–370, 2001.CrossRefGoogle Scholar
  9. 9.
    Dreischarf, M., A. Shirazi-Adl, N. Arjmand, A. Rohlmann, and H. Schmidt. Estimation of loads on human lumbar spine: a review of in vivo and computational model studies. J. Biomech. 49:833–845, 2016.CrossRefGoogle Scholar
  10. 10.
    Faber, G. S., C. C. Chang, I. Kingma, J. T. Dennerlein, and J. H. van Dieën. Estimating 3D L5/S1 moments and ground reaction forces during trunk bending using a full-body ambulatory inertial motion capture system. J. Biomech. 49:904–912, 2016.CrossRefGoogle Scholar
  11. 11.
    Faber, G. S., I. Kingma, N. Delleman, and J. van Dieën. Effect of ship motion on spinal loading during manual lifting. Ergonomics 51:1426–1440, 2008.CrossRefGoogle Scholar
  12. 12.
    Filippeschi, A., N. Schmitz, M. Miezal, G. Bleser, E. Ruffaldi, and D. Stricker. Survey of motion tracking methods based on inertial sensors: a focus on upper limb human motion. Sensors 17:1257, 2017.CrossRefGoogle Scholar
  13. 13.
    Fluit, R., M. S. Andersen, S. Kolk, N. Verdonschot, and H. F. Koopman. Prediction of ground reaction forces and moments during various activities of daily living. J. Biomech. 47:2321–2329, 2014.CrossRefGoogle Scholar
  14. 14.
    Frankenfield, D. C., W. A. Rowe, R. N. Cooney, J. S. Smith, and D. Becker. Limits of body mass index to detect obesity and predict body composition. Nutrition 17:26–30, 2001.CrossRefGoogle Scholar
  15. 15.
    Galibarov, P. E., S. Dendorfer, and J. Rasmussen. Two computational models of the lumbar spine: comparison and validation. In: ORS Annual Meeting 2011, 2011.Google Scholar
  16. 16.
    Gallagher, S., and W. S. Marras. Tolerance of the lumbar spine to shear: a review and recommended exposure limits. Clin. Biomech. 27:973–978, 2012.CrossRefGoogle Scholar
  17. 17.
    Han, K. S., T. Zander, W. R. Taylor, and A. Rohlmann. An enhanced and validated generic thoraco-lumbar spine model for prediction of muscle forces. Med. Eng. Phys. 34:709–716, 2012.CrossRefGoogle Scholar
  18. 18.
    Hansen, L., M. De Zee, J. Rasmussen, T. B. Andersen, C. Wong, and E. B. Simonsen. Anatomy and biomechanics of the back muscles in the lumbar spine with reference to biomechanical modeling. Spine (Phila. Pa. 1976) 31:1888–1899, 2006.CrossRefGoogle Scholar
  19. 19.
    Ignasiak, D., S. J. Ferguson, and N. Arjmand. A rigid thorax assumption affects model loading predictions at the upper but not lower lumbar levels. J. Biomech. 49:3074–3078, 2016.CrossRefGoogle Scholar
  20. 20.
    Karatsidis, A., G. Bellusci, H. Schepers, M. de Zee, M. Andersen, and P. Veltink. Estimation of ground reaction forces and moments during gait using only inertial motion capture. Sensors 17:75, 2016.CrossRefGoogle Scholar
  21. 21.
    Karatsidis, A., M. Jung, H. M. Schepers, G. Bellusci, M. de Zee, P. H. Veltink, and M. S. Andersen. Musculoskeletal model-based inverse dynamic analysis under ambulatory conditions using inertial motion capture. Med. Eng. Phys. 57:1–31, 2019.CrossRefGoogle Scholar
  22. 22.
    Koning, B. H. W., M. M. van der Krogt, C. T. M. Baten, and B. F. J. M. Koopman. Driving a musculoskeletal model with inertial and magnetic measurement units. Comput. Methods Biomech. Biomed. Eng. 18:1003–1013, 2015.CrossRefGoogle Scholar
  23. 23.
    Koo, T. K., and M. Y. Li. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med. 15:155–163, 2016.CrossRefGoogle Scholar
  24. 24.
    Leardini, A., A. Chiari, U. DellaCroce, and A. Cappozzo. Human movement analysis using stereophotogrammetry, Part 3 Soft tissue artifact assessment and compensation. Gait Posture 21:212–225, 2005.CrossRefGoogle Scholar
  25. 25.
    McLaughlin, P. Testing agreement between a new method and the gold standard-How do we test? J. Biomech. 46:2757–2760, 2013.CrossRefGoogle Scholar
  26. 26.
    Meldrum, D., C. Shouldice, R. Conroy, K. Jones, and M. Forward. Test–retest reliability of three dimensional gait analysis: including a novel approach to visualising agreement of gait cycle waveforms with Bland and Altman plots. Gait Posture 39:265–271, 2014.CrossRefGoogle Scholar
  27. 27.
    Moisio, K. C., D. R. Sumner, S. Shott, and D. E. Hurwitz. Normalization of joint moments during gait: a comparison of two techniques. J. Biomech. 36:599–603, 2003.CrossRefGoogle Scholar
  28. 28.
    NIOSH. Work Practices Guide for Manual Lifting. Washington, DC: NIOSH, 1981.Google Scholar
  29. 29.
    Rajaee, M. A., N. Arjmand, A. Shirazi-Adl, A. Plamondon, and H. Schmidt. Comparative evaluation of six quantitative lifting tools to estimate spine loads during static activities. Appl. Ergon. 48:22–32, 2015.CrossRefGoogle Scholar
  30. 30.
    Rasmussen, J., M. de Zee, and S. Carbes. Validation of a biomechanical model to the lumbar spine. In Proceedings, XXIInd Congress of the International Society of Biomechanics, 5–9 July 2009, Cape Town, South Africa, 2009.Google Scholar
  31. 31.
    Rasmussen, J., M. De Zee, M. Damsgaard, S. Tørholm Christensen, C. Marek, and K. Siebertz. A general method for scaling musculo-skeletal models. In 2005 International Symposium on Computer Simulation in Biomechanics, Cleveland, OH, vol. 3, 2005.Google Scholar
  32. 32.
    Ren, L., R. K. Jones, and D. Howard. Whole body inverse dynamics over a complete gait cycle based only on measured kinematics. J. Biomech. 41:2750–2759, 2008.CrossRefGoogle Scholar
  33. 33.
    Roetenberg, D., H. J. Luinge, and P. Slycke. Xsens MVN: Full 6DOF human motion tracking using miniature inertial sensors. Technical Report. Xsens Motion Technologies BV, Enschede, The Netherlands, 2013.Google Scholar
  34. 34.
    Shrout, P. E., and J. L. Fleiss. Intraclass correlations: uses in assessing rater reliability. Psychol. Bull. 86:420–428, 1979.CrossRefGoogle Scholar
  35. 35.
    Skals, S., M. K. Jung, M. Damsgaard, and M. S. Andersen. Prediction of ground reaction forces and moments during sports-related movements. Multibody Syst. Dyn. 39:175–195, 2017.CrossRefGoogle Scholar
  36. 36.
    Skals, S., K. P. Rasmussen, K. M. Bendtsen, J. Yang, and M. S. Andersen. A musculoskeletal model driven by dual Microsoft Kinect Sensor data. Multibody Syst. Dyn. 41:297–316, 2017.CrossRefGoogle Scholar
  37. 37.
    Van der Helm, F. C. T., H. E. J. Veeger, G. M. Pronk, L. H. V. Van der Woude, and R. H. Rozendal. Geometry parameters for musculoskeletal modelling of the shoulder system. J. Biomech. 25:129–144, 1992.CrossRefGoogle Scholar
  38. 38.
    Van Dieën, J. H. H., H. Weinans, and H. M. M. Toussaint. Fractures of the lumbar vertebral endplate in the etiology of low back pain: a hypothesis on the causative role of spinal compression in aspecific low back pain. Med. Hypotheses 53:246–252, 1999.CrossRefGoogle Scholar
  39. 39.
    Veeger, H. E. J., F. C. T. Van Der Helm, L. H. V. Van Der Woude, G. M. Pronk, and R. H. Rozendal. Inertia and muscle-contraction parameters for musculoskeletal modeling of the shoulder mechanism. J. Biomech. 24:615, 1991.CrossRefGoogle Scholar
  40. 40.
    Veeger, H. E. J., B. Yu, K. N. An, and R. H. Rozendal. Parameters for modeling the upper extremity. J. Biomech. 30:647–652, 1997.CrossRefGoogle Scholar
  41. 41.
    Veltink, P. H., C. Liedtke, E. Droog, and H. Van Der Kooij. Ambulatory measurement of ground reaction forces. IEEE Trans. Neural Syst. Rehabil. Eng. 13:423–427, 2005.CrossRefGoogle Scholar
  42. 42.
    Waters, T. R., V. Putz-Anderson, A. Garg, and L. J. Fine. Revised NIOSH equation for the design and evaluation of manual lifting tasks. Ergonomics 36:749–776, 1993.CrossRefGoogle Scholar
  43. 43.
    Wilke, H.-J., P. Neef, B. Hinz, H. Seidel, and L. Claes. Intradiscal pressure together with anthropometric data—a data set for the validation of models. Clin. Biomech. 16(Suppl 1):S111–S126, 2001.CrossRefGoogle Scholar
  44. 44.
    Winter, D. A. Biomechanics and Motor Control of Human Movement. Hoboken, NJ: Wiley, 2009.CrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2019

Authors and Affiliations

  1. 1.Sport Sciences, Department of Health Science and TechnologyAalborg UniversityAalborgDenmark
  2. 2.Department of Materials and ProductionAalborg UniversityAalborgDenmark
  3. 3.Musculoskeletal DisordersNational Research Centre for the Working EnvironmentCopenhagen EastDenmark

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