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Filtration Selection and Data Consilience: Distinguishing Signal from Artefact with Mechanical Impact Simulator Data

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Abstract

A large variety of data filtration techniques exist in biomechanics literature. Data filtration is both an ‘art’ and a ‘science’ to eliminate noise and retain true signal to draw conclusions that will direct future hypotheses, experimentation, and technology development. Thus, data consilience is paramount, but is dependent on filtration methodologies. In this study, we utilized ligament strain, vertical ground reaction force, and kinetic data from cadaveric impact simulations to assess data from four different filters (12 vs. 50 Hz low-pass; forward vs. zero lag). We hypothesized that 50 Hz filtered data would demonstrate larger peak magnitudes, but exhibit consilience of waveforms and statistical significance as compared to 12 Hz filtered data. Results demonstrated high data consilience for matched pair t test correlations of peak ACL strain (≥ 0.97), MCL strain (≥ 0.93) and vertical ground reaction force (≥ 0.98). Kinetics had a larger range of correlation (0.06–0.96) that was dependent on both external load application and direction of motion monitored. Coefficients of multiple correlation demonstrated high data consilience for zero lag filtered data. With respect to in vitro mechanical data, selection of low-pass filter cutoff frequency will influence both the magnitudes of discrete and waveform data. Dependent on the data type (i.e., strain and ground reaction forces), this will not likely significantly alter conclusions of statistical significance previously reported in the literature with high consilience of matched pair t-test correlations and coefficients of multiple correlation demonstrated. However, rotational kinetics are more sensitive to filtration selection and could be suspect to errors, especially at lower magnitudes.

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Abbreviations

ATS:

Anterior tibial shear

CMC:

Coefficient of multiple correlation

ITR:

Internal tibial rotation

KAM:

Knee abduction moment

vGRF:

Vertical ground reaction force

References

  1. Alonso-Betanzos, A. V Bolon-Canedo (2018) Big-data analysis, cluster analysis, and machine-learning approaches. In: Sex-specific analysis of cardivascular function, edited by P. Kerkhof, and V. Miller. Cham: Springer, 2018, pp. 607–626.

    Google Scholar 

  2. Bates, N. A., K. R. Ford, G. D. Myer, and T. E. Hewett. Kinetic and kinematic differences between first and second landings of a drop vertical jump task: implications for injury risk assessments? Clin. Biomech. 28:459–466, 2013.

    Google Scholar 

  3. Bates, N. A., K. R. Ford, G. D. Myer, and T. E. Hewett. Impact differences in ground reaction force and center of mass between the first and second landing phases of a drop vertical jump and their implications for injury risk assessment. J. Biomech. 46:1237–1241, 2013.

    PubMed  PubMed Central  Google Scholar 

  4. Bates, N. A., M. C. MejiaJaramillo, M. Vargas, A. L. McPherson, N. D. Schilaty, C. V. Nagelli, A. J. Krych, and T. E. Hewett. External loads associated with anterior cruciate ligament injuries increase the correlation between tibial slope and ligament strain during in vitro simulations of in vivo landings. Clin. Biomech. 61:84–94, 2018.

    Google Scholar 

  5. Bates, N. A., N. D. Schilaty, A. J. Krych, and T. E. Hewett. Variation in ACL and MCL strain before initial contact is dependent on injury risk level during simulated landings. Orthop J Sport. Med 7:1–9, 2019.

    Google Scholar 

  6. Bates, N. A., N. D. Schilaty, C. V. Nagelli, A. J. Krych, and T. E. Hewett. Novel mechanical impact simulator designed to generate clinically relevant anterior cruciate ligament ruptures. Clin. Biomech. 44:36–44, 2017.

    Google Scholar 

  7. Bates, N. A., N. D. Schilaty, C. V. Nagelli, A. J. Krych, and T. E. Hewett. Validation of noncontact anterior cruciate ligament tears produced by a mechanical impact simulator against the clinical presentation of injury. Am. J. Sports Med. 46:2113–2121, 2018.

    PubMed  PubMed Central  Google Scholar 

  8. Bates, N. A., N. D. Schilaty, C. V. Nagelli, A. J. Krych, and T. E. Hewett. Multiplanar loading of the knee and its influence on ACL and MCL strain during simulated landings and noncontact tears. Am J Sport. Med 47:1844–1853, 2019.

    Google Scholar 

  9. Bates, N. A., N. D. Schilaty, R. Ueno, and T. E. Hewett. Timing of strain response of the ACL and MCL relative to impulse delivery during simulated landings leading up to failure. J. Appl. Biomech. 2020. https://doi.org/10.1123/jab.2019-0308.

    Article  PubMed  Google Scholar 

  10. Beaulieu, M. L., and R. M. Palmieri-Smith. Real-time feedback on knee abduction moment does not improve frontal-plane knee mechanics during jump landings. Scand. J. Med. Sci. Sport. 24:692–699, 2014.

    CAS  Google Scholar 

  11. Bisseling, R. W., and A. L. Hof. Handling of impact forces in inverse dynamics. J. Biomech. 39:2438–2444, 2006.

    PubMed  Google Scholar 

  12. Christodoulakis, G., K. Busawon, N. Caplan, and S. Stewart. On the filtering and smoothing of biomechanical data. Commun. Syst. Networks Digit. Signal Process. (CSNDSP), 2010 7th Int. Symp. 512–516, 2010. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5580374&tag=1

  13. Ford, K. R., G. D. Myer, and T. E. Hewett. Reliability of landing 3D motion analysis: implications for longitudinal analyses. Med. Sci. Sports Exerc. 39:2021–2028, 2007.

    PubMed  Google Scholar 

  14. Fröhlich, H., R. Balling, N. Beerenwinkel, O. Kohlbacher, S. Kumar, T. Lengauer, M. H. Maathuis, Y. Moreau, S. A. Murphy, T. M. Przytycka, M. Rebhan, H. Röst, A. Schuppert, M. Schwab, R. Spang, D. Stekhoven, J. Sun, A. Weber, D. Ziemek, and B. Zupan. From hype to reality: data science enabling personalized medicine. BMC Med. 16:1–15, 2018.

    Google Scholar 

  15. Growney, E., D. Meglan, M. Johnson, T. Cahalan, and K.-N. An. Repeated measures of adult normal walking using a video tracking system. Gait Posture 6:147–162, 1997.

    Google Scholar 

  16. Hewett, T. E., K. R. Ford, Y. Y. Xu, J. Khoury, and G. D. Myer. Effectiveness of neuromuscular training based on the neuromuscular risk profile. Am. J. Sports Med. 45:2142–2147, 2017.

    PubMed  PubMed Central  Google Scholar 

  17. Hewett, T. E., G. D. Myer, K. R. Ford, R. S. Heidt, A. J. Colosimo, S. G. McLean, A. J. Van Den Bogert, M. V. Paterno, P. Succop, R. S. Heidt, Jr, A. J. Colosimo, S. G. McLean, A. J. Van Den Bogert, M. V. Paterno, and P. Succop. 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 Sport. Med 33:492–501, 2005.

    Google Scholar 

  18. Hewett, T. E., G. D. Myer, B. D. Roewer, and K. R. Ford. Letter to the editor regarding “Effect of low pass filtering on joint moments from inverse dynamics: implications for injury prevention”. J. Biomech. 45:2058–2059, 2012.

    PubMed  PubMed Central  Google Scholar 

  19. Hutchins, B. I., M. T. Davis, R. A. Meseroll, and G. M. Santangelo. Predicting translational progress in biomedical research. PLoS Biol. 17:e3000416, 2019.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Huurnink, A., D. P. Fransz, I. Kingma, and J. H. van Dieën. Comparison of a laboratory grade force platform with a Nintendo Wii Balance Board on measurement of postural control in single-leg stance balance tasks. J. Biomech. 46:1392–1395, 2013.

    PubMed  Google Scholar 

  21. Kadaba, M. P., H. K. Ramakrishnan, M. E. Wootten, J. Gainey, G. Gorton, and G. V. B. Cochran. Repeatability of kinematic, kinetic, and electromyographic data in normal adult gait. J. Orthop. Res. 7:849–860, 1989.

    CAS  PubMed  Google Scholar 

  22. Kakavas, G., N. Malliaropoulos, R. Pruna, and N. Maffulli. Artificial intelligence. A tool for sports trauma prediction. Injury 2019. https://doi.org/10.1016/j.injury.2019.08.033.

    Article  PubMed  Google Scholar 

  23. Kiapour, A. M., C. K. Demetropoulos, A. Kiapour, C. E. Quatman, S. C. Wordeman, V. K. Goel, and T. E. Hewett. Strain response of the anterior cruciate ligament to uniplanar and multiplanar loads during simulated landings: implications for injury mechanism. Am. J. Sport. Med. 44:2087–2096, 2016.

    Google Scholar 

  24. Kiapour, A., A. M. Kiapour, V. Kaul, C. E. Quatman, S. C. Wordeman, T. E. Hewett, C. K. Demetropoulos, and V. K. Goel. Finite element model of the knee for investigation of injury mechanisms: development and validation. J. Biomech. Eng. 136:11002, 2014.

    Google Scholar 

  25. Kiernan, D., R. H. Miller, B. S. Baum, H. J. Kwon, and J. K. Shim. Amputee locomotion: Frequency content of prosthetic vs. intact limb vertical ground reaction forces during running and the effects of filter cut-off frequency. J. Biomech. 60:248–252, 2017.

    PubMed  Google Scholar 

  26. Kristianslund, E., T. Krosshaug, and A. J. Van Den Bogert. Effect of low pass filtering on joint moments from inverse dynamics: implications for injury prevention. J. Biomech. 45:666–671, 2012.

    PubMed  Google Scholar 

  27. Kristianslund, E., T. Krosshaug, and A. J. Van Den Bogert. Artefacts in measuring joint moments may lead to incorrect clinical conclusions: the nexus between science (biomechanics) and sports injury prevention!. Br. J. Sports Med. 47:470–473, 2013.

    PubMed  Google Scholar 

  28. McPherson, A. L., N. A. Bates, N. D. Schilaty, C. V. Nagelli, A. J. Krych, and T. E. Hewett. Ligament strain response between lower extremity contralateral pairs during in vitro landing simulation. Orthop. J Sport. Med. 6:1–7, 2018.

    Google Scholar 

  29. Myer, G. D., K. R. Ford, J. L. Brent, and T. E. Hewett. Differential neuromuscular training effects on ACL injury risk factors in “high-risk” versus “low-risk” athletes. BMC Musculoskelet. Disord. 8:1–7, 2007.

    Google Scholar 

  30. Navacchia, A., N. A. Bates, N. D. Schilaty, A. J. Krych, and T. E. Hewett. Knee abduction and internal rotation moments increase ACL force during landing through the posterior slope of the tibia. J. Orthop. Res. 37:1730–1742, 2019.

    PubMed  PubMed Central  Google Scholar 

  31. Pollard, C. D., K. M. Stearns, A. T. Hayes, B. C. Heiderscheit, C. D. Pollard, and K. M. Stearns. Altered lower extremity movement variability in female soccer players during side-step cutting after anterior cruciate ligament reconstruction. Am. J. Sports Med. 43:460–465, 2015.

    PubMed  Google Scholar 

  32. Quatman, C. E., C. C. Quatman, and T. E. Hewett. Prediction and prevention of musculoskeletal injury: a paradigm shift in methodology. Br. J. Sports Med. 43:1100–1107, 2009.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Ratner, B. The correlation coefficient: Its values range between 1/1, or do they. J. Target. Meas. Anal. Mark. 17:139–142, 2009.

    Google Scholar 

  34. Robertson, D. G. E., and J. J. Dowling. Design and responses of Butterworth and critically damped digital filters. J. Electromyogr. Kinesiol. 13:569–573, 2003.

    PubMed  Google Scholar 

  35. Roewer, B. D., K. R. Ford, G. D. Myer, and T. E. Hewett. The “Impact” of force filtering cut-off frequency on the peak knee abduction moment during landing: artefact or ‘artifiction’? Br. J. Sport. Med. 48:464–468, 2014.

    Google Scholar 

  36. Sanal, M., K. Paul, S. Kumar, and N. Ganguly. Artificial intelligence and deep learning: the future of medicine and medical practice. J. Assoc. Phys. India 67:71–73, 2019.

    Google Scholar 

  37. Schilaty, N. D., N. A. Bates, A. J. Krych, and T. E. Hewett. Frontal plane medial collateral ligament strain characteristics concurrent to anterior cruciate ligament failure. Am. J. Sport. Med. 47:2143–2150, 2019.

    Google Scholar 

  38. Schilaty, N. D., N. A. Bates, C. V. Nagelli, A. J. Krych, and T. E. Hewett. Sex-based differences of knee kinetics that occur with anterior cruciate ligament strain on cadaveric impact simulations. Orthop. J. Sport. Med. 6:1–7, 2018.

    Google Scholar 

  39. Schilaty, N. D., N. A. Bates, C. V. Nagelli, A. J. Krych, and T. E. Hewett. Sex-based differences of medial collateral and anterior cruciate ligament strains with cadaveric impact simulations. Orthop. J. Sport. Med. 6:1–8, 2018.

    Google Scholar 

  40. Shultz, S. J., and R. J. Schmitz. Effects of transverse and frontal plane knee laxity on hip and knee neuromechanics during drop landings. Am. J. Sport. Med. 37:1821–1830, 2009.

    Google Scholar 

  41. Shultz, S. J., R. J. Schmitz, A. D. Nguyen, and B. J. Levine. Joint laxity is related to lower extremity energetics during a drop jump landing. Med. Sci. Sports Exerc. 42:771–780, 2010.

    PubMed  PubMed Central  Google Scholar 

  42. Sinclair, J., P. John Taylor, and S. Jane Hobbs. Digital filtering of three-dimensional lower extremity kinematics: an assessment. J. Hum. Kinet. 39:25–36, 2013.

    PubMed  PubMed Central  Google Scholar 

  43. Tomescu, S. S., R. Bakker, T. A. C. Beach, and N. Chandrashekar. The effects of filter cutoff frequency on musculoskeletal simulations of high-impact movements. J. Appl. Biomech. 34:336–341, 2018.

    PubMed  Google Scholar 

  44. Turner, C., S. Crow, T. Crowther, B. Keating, T. Saupan, J. Pyfer, K. Vialpando, and S.-P. Lee. Preventing non-contact ACL injuries in female athletes: what can we learn from dancers? Phys. Ther. Sport 31:1–8, 2018.

    PubMed  Google Scholar 

  45. Ueno, R., T. Ishida, M. Yamanaka, S. Taniguchi, R. Ikuta, M. Samukawa, H. Saito, and H. Tohyama. Quadriceps force and anterior tibial force occur obviously later than vertical ground reaction force: a simulation study. BMC Musculoskelet. Disord. 18:1–8, 2017.

    Google Scholar 

  46. Weinhandl, J. T., B. S. R. Armstrong, T. P. Kusik, R. T. Barrows, and K. M. O. Connor. Validation of a single camera three-dimensional motion tracking system. J. Biomech. 43:1437–1440, 2010.

    PubMed  PubMed Central  Google Scholar 

  47. Winter, D. A. Biomechanics and motor control of human movement. Hoboken: Wiley, 2009.

    Google Scholar 

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Acknowledgments

NIH funding include: K12HD065987 and L30AR070273 to NDS, R01AR055563 to NAB, and R01AR056259 to TEH.

Conflict of interest

All authors declare that there are no conflicts of interest.

Author information

Author notes

  1. Nathan D. Schilaty and Nathaniel A. Bates: Shared first authorship because of equal contribution to this study.

Authors and Affiliations

  1. Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA

    Nathan D. Schilaty, Nathaniel A. Bates & Ryo Ueno

  2. Sports Medicine Center, Mayo Clinic, Rochester, MN, USA

    Nathan D. Schilaty, Nathaniel A. Bates & Ryo Ueno

  3. Department of Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, USA

    Nathan D. Schilaty & Nathaniel A. Bates

  4. Department of Physical Medicine & Rehabilitation, Mayo Clinic, Rochester, MN, USA

    Nathan D. Schilaty

  5. Biomechanics Laboratories, 200 First Street SW, Rochester, MN, 55905, USA

    Nathan D. Schilaty & Nathaniel A. Bates

  6. Sparta Science, Menlo Park, CA, USA

    Timothy E. Hewett

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  1. Nathan D. Schilaty
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Correspondence to Nathan D. Schilaty.

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Associate Editor Eiji Tanaka oversaw the review of this article.

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Schilaty, N.D., Bates, N.A., Ueno, R. et al. Filtration Selection and Data Consilience: Distinguishing Signal from Artefact with Mechanical Impact Simulator Data. Ann Biomed Eng 49, 334–344 (2021). https://doi.org/10.1007/s10439-020-02562-5

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  • DOI: https://doi.org/10.1007/s10439-020-02562-5

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