Dynamic Joint Motions in Occupational Environments as Indicators of Potential Musculoskeletal Injury Risk

in Journal of Applied Biomechanics

Click name to view affiliation

Jonathan S. Dufour The Ohio State University

Search for other papers by Jonathan S. Dufour in
Current site
Google Scholar
PubMed Close
*
,
Alexander M. Aurand The Ohio State University

Search for other papers by Alexander M. Aurand in
Current site
Google Scholar
PubMed Close
*
,
Eric B. Weston The Ohio State University

Search for other papers by Eric B. Weston in
Current site
Google Scholar
PubMed Close
*
,
Christopher N. Haritos The Ohio State University

Search for other papers by Christopher N. Haritos in
Current site
Google Scholar
PubMed Close
*
,
Reid A. Souchereau The Ohio State University

Search for other papers by Reid A. Souchereau in
Current site
Google Scholar
PubMed Close
*
, and
William S. Marras The Ohio State University

Search for other papers by William S. Marras in
Current site
Google Scholar
PubMed Close
*
DOI:
https://doi.org/10.1123/jab.2020-0213
Keywords:
inertial measurement unit; motion capture; kinematics; motion analysis
First Published Online:
09 Mar 2021
In Print:
Volume 37: Issue 3
Page Range:
196–203
Restricted access

The objective of this study was to test the feasibility of using a pair of wearable inertial measurement unit (IMU) sensors to accurately capture dynamic joint motion data during simulated occupational conditions. Eleven subjects (5 males and 6 females) performed repetitive neck, low-back, and shoulder motions simulating low- and high-difficulty occupational tasks in a laboratory setting. Kinematics for each of the 3 joints were measured via IMU sensors in addition to a “gold standard” passive marker optical motion capture system. The IMU accuracy was benchmarked relative to the optical motion capture system, and IMU sensitivity to low- and high-difficulty tasks was evaluated. The accuracy of the IMU sensors was found to be very good on average, but significant positional drift was observed in some trials. In addition, IMU measurements were shown to be sensitive to differences in task difficulty in all 3 joints (P < .05). These results demonstrate the feasibility for using wearable IMU sensors to capture kinematic exposures as potential indicators of occupational injury risk. Velocities and accelerations demonstrate the most potential for developing risk metrics since they are sensitive to task difficulty and less sensitive to drift than rotational position measurements.

Dufour, Aurand, Weston, Haritos, Souchereau, and Marras are with the Spine Research Institute, The Ohio State University, Columbus, OH, USA. Dufour, Aurand, Weston, Souchereau, and Marras are also with the Department of Integrated Systems Engineering, The Ohio State University, Columbus, OH, USA.

Marras (marras.1@osu.edu) is corresponding author.
  • Collapse
  • Expand

Journal of Applied Biomechanics

Cover Journal of Applied Biomechanics
  • 1.

    BLS. Nonfatal Cases Involving Days Away From Work: Selected Characteristics (2011 Forward). Washington, DC: U.S. Bureau of Labor Statistics; 2020.

    • Search Google Scholar
    • Export Citation
  • 2.

    Dieleman JL, Baral R, Birger M, et al. US spending on personal health care and public health, 1996–2013. JAMA. 2016;316(24):2627–2646. PubMed ID: 28027366 doi:10.1001/jama.2016.16885

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Dagenais S, Caro J, Haldeman S. A systematic review of low back pain cost of illness studies in the United States and internationally. Spine J. 2008;8(1):8–20. PubMed ID: 18164449 doi:10.1016/j.spinee.2007.10.005

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4.

    Martin BI, Deyo RA, Mirza SK, et al. Expenditures and health status among adults with back and neck problems. JAMA. 2008;299(6):656–664. PubMed ID: 18270354 doi:10.1001/jama.299.6.656

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5.

    Meislin RJ, Sperling JW, Stitik TP. Persistent shoulder pain: epidemiology, pathophysiology, and diagnosis. Am J Orthop. 2005;34(12 suppl):5–9. PubMed ID: 16450690

    • Search Google Scholar
    • Export Citation
  • 6.

    NRC. Musculoskeletal Disorders and the Workplace: Low Back and Upper Extremities. Washington, DC: National Academies Press; 2001.

  • 7.

    Marras WS, Parnianpour M, Ferguson SA, et al. The classification of anatomic- and symptom-based low back disorders using motion measure models. Spine. 1995;20(23):2531–2546. PubMed ID: 8610248 doi:10.1097/00007632-199512000-00013

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Marras WS, Lavender SA, Leurgans SE, et al. The role of dynamic three-dimensional trunk motion in occupationally-related low back disorders. The effects of workplace factors, trunk position, and trunk motion characteristics on risk of injury. Spine. 1993;18(5):617–628. PubMed ID: 8484154 doi:10.1097/00007632-199304000-00015

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9.

    Fathallah FA, Marras WS, Parnianpour M. The role of complex, simultaneous trunk motions in the risk of occupation-related low back disorders. Spine. 1998;23(9):1035–1042. PubMed ID: 9589543 doi:10.1097/00007632-199805010-00014

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    Ferguson SA, Marras WS. A literature review of low back disorder surveillance measures and risk factors. Clin Biomech. 1997;12(4):211–226. PubMed ID: 11415726 doi:10.1016/S0268-0033(96)00073-3

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 11.

    Marras WS, Lavender SA, Leurgans SE, et al. Biomechanical risk factors for occupationally related low back disorders. Ergonomics. 1995;38(2):377–410. PubMed ID: 7895740 doi:10.1080/00140139508925111

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    Sommerich CM, McGlothlin JD, Marras WS. Occupational risk factors associated with soft tissue disorders of the shoulder: a review of recent investigations in the literature. Ergonomics. 1993;36(6):697–717. PubMed ID: 8513776 doi:10.1080/00140139308967931

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13.

    Aurand AM, Dufour JS, Marras WS. Accuracy map of an optical motion capture system with 42 or 21 cameras in a large measurement volume. J Biomech. 2017;58:237–240. PubMed ID: 28549599 doi:10.1016/j.jbiomech.2017.05.006

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 14.

    Ong A, Harris IS, Hamill J. The efficacy of a video-based marker-less tracking system for gait analysis. Comput Methods Biomech Biomed Eng. 2017;20(10):1089–1095. PubMed ID: 28569549 doi:10.1080/10255842.2017.1334768

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15.

    Sandau M, Koblauch H, Moeslund TB, Aanæs H, Alkjær T, Simonsen EB. Markerless motion capture can provide reliable 3D gait kinematics in the sagittal and frontal plane. Med Eng Phys. 2014;36(9):1168–1175. PubMed ID: 25085672 doi:10.1016/j.medengphy.2014.07.007

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16.

    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. PubMed ID: 24595273 doi:10.1371/journal.pone.0087640

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    Wirth MA, Fischer G, Verdú J, Reissner L, Balocco S, Calcagni M. Comparison of a new inertial sensor based system with an optoelectronic motion capture system for motion analysis of healthy human wrist joints. Sensors. 2019;19(23):5297. PubMed ID: 31805699 doi:10.3390/s19235297

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 18.

    Beange KHE, Chan ADC, Beaudette SM, Graham RB. Concurrent validity of a wearable IMU for objective assessments of functional movement quality and control of the lumbar spine. J Biomech. 2019;97:109356. PubMed ID: 31668717 doi:10.1016/j.jbiomech.2019.109356

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    Faber GS, Chang C-C, Rizun P, Dennerlein JT. A novel method for assessing the 3-D orientation accuracy of inertial/magnetic sensors. J Biomech. 2013;46(15):2745–2751. PubMed ID: 24016678 doi:10.1016/j.jbiomech.2013.07.029

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20.

    Godwin A, Agnew M, Stevenson J. Accuracy of inertial motion sensors in static, quasistatic, and complex dynamic motion. J Biomech Eng. 2009;131(11):114501. PubMed ID: 20353265 doi:10.1115/1.4000109

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21.

    Wong WY, Wong MS. Trunk posture monitoring with inertial sensors. Eur Spine J. 2008;17(5):743–753. PubMed ID: 18196296 doi:10.1007/s00586-008-0586-0

  • 22.

    El-Gohary M, McNames J. Shoulder and elbow joint angle tracking with inertial sensors. IEEE Trans Biomed Eng. 2012;59(9):2635–2641. PubMed ID: 22911538 doi:10.1109/TBME.2012.2208750

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23.

    Zhou H, Hu H. Upper limb motion estimation from inertial measurements. Int J Inf Technol. 2007;13(1):1–14.

  • 24.

    Ferrari A, Cutti AG, Garofalo P, et al. First in vivo assessment of “Outwalk”: a novel protocol for clinical gait analysis based on inertial and magnetic sensors. Med Biol Eng Comput. 2010;48(1):1–15. PubMed ID: 19911215 doi:10.1007/s11517-009-0544-y

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.

    Giansanti D, Maccioni G, Benvenuti F, Macellari V. Inertial measurement units furnish accurate trunk trajectory reconstruction of the sit-to-stand manoeuvre in healthy subjects. Med Biol Eng Comput. 2007;45(10):969–976. PubMed ID: 17653580 doi:10.1007/s11517-007-0224-8

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26.

    Picerno P, Cereatti A, Cappozzo A. Joint kinematics estimate using wearable inertial and magnetic sensing modules. Gait Posture. 2008;28(4):588–595. PubMed ID: 18502130 doi:10.1016/j.gaitpost.2008.04.003

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27.

    Zhang J-T, Novak AC, Brouwer B, Li Q. Concurrent validation of Xsens MVN measurement of lower limb joint angular kinematics. Physiol Meas. 2013;34(8):N63–N69. PubMed ID: 23893094 doi:10.1088/0967-3334/34/8/N63

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28.

    Ajdaroski M, Tadakala R, Nichols L, Esquivel A. Validation of a device to measure knee joint angles for a dynamic movement. Sensors. 2020;20(6):1747. PubMed ID: 32245187 doi:10.3390/s20061747

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 29.

    Fantozzi S, Giovanardi A, Magalhães FA, Di Michele R, Cortesi M, Gatta G. Assessment of three-dimensional joint kinematics of the upper limb during simulated swimming using wearable inertial-magnetic measurement units. J Sports Sci. 2016;34(11):1073–1080. PubMed ID: 26367468 doi:10.1080/02640414.2015.1088659

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30.

    Kim S, Nussbaum MA. Performance evaluation of a wearable inertial motion capture system for capturing physical exposures during manual material handling tasks. Ergonomics. 2013;56(2):314–326. PubMed ID: 23231730 doi:10.1080/00140139.2012.742932

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31.

    Luinge HJ, Veltink PH. Measuring orientation of human body segments using miniature gyroscopes and accelerometers. Med Biol Eng Comput. 2005;43(2):273–282. PubMed ID: 15865139 doi:10.1007/BF02345966

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32.

    Plamondon A, Delisle A, Larue C, et al. Evaluation of a hybrid system for three-dimensional measurement of trunk posture in motion. Appl Ergon. 2007;38(6):697–712. PubMed ID: 17382283 doi:10.1016/j.apergo.2006.12.006

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33.

    Robert-Lachaine X, Mecheri H, Larue C, Plamondon A. Validation of inertial measurement units with an optoelectronic system for whole-body motion analysis. Med Biol Eng Comput. 2017;55(4):609–619. PubMed ID: 27379397 doi:10.1007/s11517-016-1537-2

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34.

    Robert-Lachaine X, Mecheri H, Larue C, Plamondon A. Effect of local magnetic field disturbances on inertial measurement units accuracy. Appl Ergon. 2017;63:123–132. PubMed ID: 28502401 doi:10.1016/j.apergo.2017.04.011

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35.

    Robert-Lachaine X, Mecheri H, Muller A, Larue C, Plamondon A. Validation of a low-cost inertial motion capture system for whole-body motion analysis. J Biomech. 2020;99:109520. PubMed ID: 31787261 doi:10.1016/j.jbiomech.2019.109520

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36.

    Schall MC, Jr, Fethke NB, Chen H, Oyama S, Douphrate DI. Accuracy and repeatability of an inertial measurement unit system for field-based occupational studies. Ergonomics. 2016;59(4):591–602. PubMed ID: 26256753 doi:10.1080/00140139.2015.1079335

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37.

    Schiefer C, Ellegast RP, Hermanns I, et al. Optimization of inertial sensor-based motion capturing for magnetically distorted field applications. J Biomech Eng. 2014;136(12):121008. PubMed ID: 25321344 doi:10.1115/1.4028822

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38.

    Faber GS, Kingma I, Chang CC, Dennerlein JT, van Dieën JH. Validation of a wearable system for 3D ambulatory L5/S1 moment assessment during manual lifting using instrumented shoes and an inertial sensor suit. J Biomech. 2020;102:109671. PubMed ID: 32143885 doi:10.1016/j.jbiomech.2020.109671

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 39.

    Koopman AS, Kingma I, Faber GS, Bornmann J, van Dieën JH. Estimating the L5S1 flexion/extension moment in symmetrical lifting using a simplified ambulatory measurement system. J Biomech. 2018;70:242–248. PubMed ID: 29054609 doi:10.1016/j.jbiomech.2017.10.001

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40.

    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. 2019;20(1):130. PubMed ID: 31878224 doi:10.3390/s20010130

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 41.

    De Brabandere A, Emmerzaal J, Timmermans A, Jonkers I, Vanwanseele B, Davis J. A machine learning approach to estimate hip and knee joint loading using a mobile phone-embedded IMU. Front Bioeng Biotechnol. 2020;8:320. PubMed ID: 32351952 doi:10.3389/fbioe.2020.00320

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 42.

    Conforti I, Mileti I, Del Prete Z, Palermo E. Measuring biomechanical risk in lifting load tasks through wearable system and machine-learning approach. Sensors. 2020;20(6):1557. PubMed ID: 32168844 doi:10.3390/s20061557

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 43.

    Ricci L, Taffoni F, Formica D. On the orientation error of IMU: investigating static and dynamic accuracy targeting human motion. PLoS One. 2016;11(9):e0161940. PubMed ID: 27612100 doi:10.1371/journal.pone.0161940

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44.

    Roetenberg D, Baten C, Veltink P. Estimating body segment orientation by applying inertial and magnetic sensing near ferromagnetic materials. IEEE Trans Neural Syst Rehabil Eng. 2007;15(3):469–471. PubMed ID: 17894280 doi:10.1109/TNSRE.2007.903946

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45.

    Mundt M, Koeppe A, David S, et al. Estimation of gait mechanics based on simulated and measured IMU data using an artificial neural network. Front Bioeng Biotechnol. 2020;8:41. PubMed ID: 32117923 doi:10.3389/fbioe.2020.00041

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 46.

    Fryar CD, Kruszon-Moran D, Gu Q, Ogden CL. Mean body weight, height, waist circumference, and body mass index among adults: United States, 1999–2000 through 2015–2016. Natl Health Stat Report. 2018;122:1–16. PubMed ID: 30707668

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 1940 484 14
Full Text Views 217 51 3
PDF Downloads 124 6 0