Skip to main content
SpringerLink
Log in

Development of a Wearable IMU System for Automatically Assessing Lifting Risk Factors

  • Conference paper
  • First Online:
Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Posture, Motion and Health (HCII 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12198))

Included in the following conference series:

Abstract

The objective of this study was to develop a five inertial measurement unit (IMU) sensor system attached to the human body for automatically identifying the duration of the lifting task (LD) performed symmetrically with two hands at various hand locations relative to the body, and three other lifting risk variables including the trunk flexion angle (T), the vertical distance (V) and horizontal distance (H) of the lifting task defined by the revised National Institute for Occupational Safety and Health lifting equation (RNLE). The algorithm that processed the IMU data consisted of two modules: the synchronization module that extracted the synchronization feature of wrists’ motion data to identify the lifting event; and the lifting variable calculations module that employed a body segment length ratio model for calculating the risk variables. The variable calculation module was further modified to include subjects’ body segment length information for improved accuracy. The wearable system was validated by motion data collected by a laboratory grade motion capture system on 10 human subjects performing 360 lifting trials. Results showed that the model performed well for determining the LD (~1 s error) and T (~2° error). However, the mean errors for V and H were large (33 and 6.5 cm, respectively). Inclusion of subjects’ five body segment length measurements improved the mean errors of V and H to 14 and 2.2 cm, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Liberty Mutual Research Institute for Safety: Liberty Mutual Workplace Safety Index. Liberty Mutual 175 Berkeley St., Boston, MA 02116 (2014)

    Google Scholar 

  2. Luo, X., Pietrobon, R., Sun, S.X., Liu, G.G., Hey, L.: Estimates and patterns of direct health care expenditures among individuals with back pain in the United States. Spine 29(1), 79–86 (2004)

    Article  Google Scholar 

  3. Bernard, B.P.: Musculoskeletal Disorders and Workplace Factors: A Critical Review of Epidemiologic Evidence for Work-Related Musculoskeletal Disorders of the Neck, Upper Extremity, and Low Back. U.S. Department of Health and Human Services, Center for Disease Control and Prevention, National Institute for Occupational Health and Safety, Cincinnati OH (1997)

    Google Scholar 

  4. National Research Council: Musculoskeletal Disorders and the Workplace: Low Back and Upper Extremities, Washington, DC (2001)

    Google Scholar 

  5. da Costa, B.R., Vieira, E.R.: Risk factors for work-related musculoskeletal disorders: a systematic review of recent longitudinal studies. Am. J. Ind. Med. 53(3), 285–323 (2010)

    Google Scholar 

  6. Lu, M.L., Waters, T.R., Krieg, E., Werren, D.: Efficacy of the revised NIOSH lifting equation to predict risk of low-back pain associated with manual lifting: a one-year prospective study. Hum. Factors 56(1), 73–85 (2014)

    Article  Google Scholar 

  7. Callaghan, J.P., Salewytsch, A.J., Andrews, D.M.: An evaluation of predictive methods for estimating cumulative spinal loading. Ergonomics 44(9), 825–837 (2001)

    Article  Google Scholar 

  8. Lu, M.L., Waters, T., Werren, D.: Development of human posture simulation method for assessing posture angles and spinal loads. Hum. Factors Ergon. Manuf. Serv. Ind. 25(1), 123–136 (2015)

    Google Scholar 

  9. Radwin, R.G., Lin, M.L.: An analytical method for characterizing repetitive motion and postural stress using spectral-analysis. Ergonomics 36(4), 379–389 (1993)

    Article  Google Scholar 

  10. Bhattacharya, A., Warren, J., Teuschler, J., Dimov, M., Medvedovic, M., Lemasters, G.: Development and evaluation of a microprocessor-based ergonomic dosimeter for evaluating carpentry tasks. Appl. Ergon. 30(6), 543–553 (1999)

    Article  Google Scholar 

  11. Marras, W.S., Fathallah, F.A., Miller, R.J., Davis, S.W., Mirka, G.A.: Accuracy of a three-dimensional lumbar motion monitor for recording dynamic trunk motion characteristics. Int. J. Ind. Ergon. 9(1), 75–87 (1992)

    Article  Google Scholar 

  12. Freivalds, A., Kong, Y., You, H., Park, S.: A comprehensive risk assessment model for work-related musculoskeletal disorders of the upper extremities. In: Proceedings of Human Factors and Ergonomics Society Annual Meeting, vol. 44, no. 31, pp. 5-728–5-731. SAGE Publications, Los Angeles (2000)

    Google Scholar 

  13. Battini, D., Persona, A., Sgarbossa, F.: Innovative real-time system to integrate ergonomic evaluations into warehouse design and management. Comput. Ind. Eng. 77, 1–10 (2014)

    Article  Google Scholar 

  14. He, Z., Jin, L.: Activity recognition from acceleration data based on discrete cosine transform and SVM. In: 2009 IEEE International Conference on Systems, Man and Cybernetics, San Antonio TX, pp. 5041–5044. IEEE (2009)

    Google Scholar 

  15. Dahlqvist, C., Hansson, G.Å., Forsman, M.: Validity of a small low-cost triaxial accelerometer with integrated logger for uncomplicated measurements of postures and movements of head, upper back and upper arms. Appl. Ergon. 55, 108–116 (2016)

    Article  Google Scholar 

  16. Schall Jr., M.C., Fethke, N.B., Chen, H., Oyama, S., Douphrate, D.I.: Accuracy and repeatability of an inertial measurement unit system for field-based occupational studies. Ergonomics 59(4), 591–602 (2015)

    Article  Google Scholar 

  17. Brents, C., Hischke, M., Reiser, R., Rosecrance, J.: Low back biomechanics of keg handling using inertial measurement units. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds.) IEA 2018. AISC, vol. 825, pp. 71–81. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-96068-5_8

    Chapter  Google Scholar 

  18. Zhou, H., Hu, H., Tao, Y.: Inertial measurements of upper limb motion. Med. Biol. Eng. Comput. 44(6), 479–487 (2006)

    Article  Google Scholar 

  19. Zhou, H., Stone, T., Hu, H., Harris, N.: Use of multiple wearable inertial sensors in upper limb motion tracking. Med. Eng. Phys. 30(1), 123–133 (2008)

    Article  Google Scholar 

  20. Zhou, H., Hu, H.: Reducing drifts in the inertial measurements of wrist and elbow positions. IEEE Trans. Instrum. Meas. 59(3), 575–585 (2010)

    Article  Google Scholar 

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

    Google Scholar 

  22. Cutti, A.G., Giovanardi, A., Rocchi, L., Davalli, A., Sacchetti, R.: Ambulatory measurement of shoulder and elbow kinematics through inertial and magnetic sensors. Med. Biol. Eng. Comput. 46(2), 169–178 (2008)

    Article  Google Scholar 

  23. de Vries, W., Veeger, H., Cutti, A., Baten, C., van der Helm, F.: Functionally interpretable local coordinate systems for the upper extremity using inertial & magnetic measurement systems. J. Biomech. 43(10), 1983–1988 (2010)

    Article  Google Scholar 

  24. El-Gohary, M., McNames, J.: Shoulder and elbow joint angle tracking with inertial sensors. IEEE Trans. Biomed. Eng. 59(9), 2635–2641 (2012)

    Article  Google Scholar 

  25. Vignais, N., Miezal, M., Bleser, G., Mura, K., Gorecky, D., Marin, F.: Innovative system for real-time ergonomic feedback in industrial manufacturing. Appl. Ergon. 44(4), 566–574 (2013)

    Article  Google Scholar 

  26. Caputo, F., D’Amato, E., Spada, S., Sessa, F., Losardo, M.: Upper body motion tracking system with inertial sensors for ergonomic issues in industrial environments. In: Goonetilleke, R., Karwowski, W. (eds.) Advances in Physical Ergonomics and Human Factors. AISC, vol. 489, pp. 801–812. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41694-6_77

    Chapter  Google Scholar 

  27. Morrow, M.B., Lowndes, B., Fortune, E., Kaufman, K.R., Hallbeck, M.: Validation of inertial measurement units for upper body kinematics. J. Appl. Biomech. 33(3), 227–232 (2017)

    Article  Google Scholar 

  28. Chen, H., Schall Jr., M.C., Fethke, N.: Accuracy of angular displacements and velocities from inertial-based inclinometers. Appl. Ergon. 67, 151–161 (2018)

    Article  Google Scholar 

  29. Peppoloni, L., Filippeschi, A., Ruffaldi, E., Avizzano, C.A.: A novel 7 degrees of freedom model for upper limb kinematic reconstruction based on wearable sensors. In: 2013 IEEE 11th International Symposium on Intelligent Systems and Informatics, Subotica Serbia, pp. 105–110. IEEE (2013)

    Google Scholar 

  30. Jasiewicz, J.M., Treleaven, J., Condie, P., Jull, G.: Wireless orientation sensors: their suitability to measure head movement for neck pain assessment. Manual Ther. 12(4), 380–385 (2007)

    Article  Google Scholar 

  31. Theobald, P.S., Jones, M.D., Williams, J.M.: Do inertial sensors represent a viable method to reliably measure cervical spine range of motion? Manual Ther. 17(1), 92–96 (2012)

    Article  Google Scholar 

  32. Duc, C., Salvia, P., Lubansu, A., Feipel, V., Aminian, K.: A wearable inertial system to assess the cervical spine mobility: comparison with an optoelectronic-based motion capture evaluation. Med. Eng. Phys. 36(1), 49–56 (2014)

    Article  Google Scholar 

  33. Favre, J., Jolles, B.M., Aissaoui, R., Aminian, K.: Ambulatory measurement of 3D knee joint angle. J. Biomech. 41(5), 1029–1035 (2008)

    Article  Google Scholar 

  34. Picerno, P., Cereatti, A., Cappozzo, A.: Joint kinematics estimate using wearable inertial and magnetic sensing modules. Gait Posture 28(4), 588–595 (2008)

    Article  Google Scholar 

  35. Ferrari, A., 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. 48(1), 1–15 (2010)

    Article  Google Scholar 

  36. Fong, D.T., Chan, Y.Y.: The use of wearable inertial motion sensors in human lower limb biomechanics studies: a systematic review. Sensors 10(12), 11556–11565 (2010)

    Article  Google Scholar 

  37. Bolink, S.A.A.N., et al.: Validity of an inertial measurement unit to assess pelvic orientation angles during gait, sit-stand transfers and set-up transfers: comparison with an optoelectronic motion capture system. Med. Eng. Phys. 38(3), 225–231 (2016)

    Article  Google Scholar 

  38. Beravs, T., Rebersek, P., Novak, D., Podobnik, J., Munih, M.: Development and validation of a wearable inertial measurement system for use with lower limb exoskeletons. In: 2011 11th IEEE-RAS International Conference on Humanoid Robots, Bled Slovenia, pp. 212–217. IEEE (2011)

    Google Scholar 

  39. O’Reilly, M.A., Whelan, D.F., Ward, T.E., Delahunt, E., Caulfield, B.: Classification of lunge biomechanics with multiple and individual inertial measurement units. Sports Biomech. 16(3), 342–360 (2016)

    Article  Google Scholar 

  40. Teufl, W., Miezal, M., Taetz, B., Frohlich, M., Bleser, G.: Validity of inertial sensor-based 3D joint kinematics of static and dynamic sport and physiotherapy specific movements. PLOS One 14(2), 1–18 (2019)

    Article  Google Scholar 

  41. Lee, R.Y.W., Laprade, J., Fung, E.H.K.: A real-time gyroscopic system for three-dimensional measurement of lumbar spine motion. Med. Eng. Phys. 25(10), 817–824 (2003)

    Article  Google Scholar 

  42. Goodvin, C., Park, E.J., Huang, K., Sakaki, K.: Development of a real-time three-dimensional spinal motion measurement system for clinical practice. Med. Biol. Eng. Comput. 44(12), 1061–1075 (2006)

    Article  Google Scholar 

  43. 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. 45(10), 969–976 (2007)

    Article  Google Scholar 

  44. Plamondon, A., et al.: Evaluation of a hybrid system for three-dimensional measurement of trunk posture in motion. Appl. Ergon. 38(6), 697–712 (2007)

    Article  Google Scholar 

  45. Roetenberg, D., Slycke, P.J., Veltink, P.H.: Ambulatory position and orientation tracking fusing magnetic and inertial sensing. IEEE Trans. Biomed. Eng. 54(5), 883–890 (2007)

    Article  Google Scholar 

  46. Kim, S., Nussbaum, M.A.: Performance evaluation of a wearable inertial motion capture system for capturing physical exposures during manual material handling tasks. Ergonomics 56(2), 314–326 (2013)

    Article  Google Scholar 

  47. Bauer, C.M., et al.: Concurrent validity and reliability of a novel wireless inertial measurement system to assess trunk movement. J. Electromyogr. Kinesiol. 25(5), 782–790 (2015)

    Article  Google Scholar 

  48. Bergamini, E., Guillon, P., Camomilla, V., Pillet, H., Skalli, W., Cappozzo, A.: Trunk inclination estimate during the sprint start using an inertial measurement unit: a validation study. J. Appl. Biomech. 29(5), 622–627 (2013)

    Article  Google Scholar 

  49. Monaco, M.G.L., et al.: Biomechanical overload evaluation in manufacturing: a novel approach with sEMG and inertial motion capture integration. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds.) IEA 2018. AISC, vol. 818, pp. 719–726. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-96098-2_88

    Chapter  Google Scholar 

  50. Brodie, M.A., Walmsley, A., Page, W.: Dynamic accuracy of inertial measurement units during simple pendulum motion. Comput. Methods Biomech. Biomed. Eng. 11(3), 235–242 (2008)

    Article  Google Scholar 

  51. Brodie, M.A., Walmsley, A., Page, W.: Fusion motion capture: a prototype system using inertial measurement units and GPS for the biomechanical analysis of ski racing. Sports Technol. 1(1), 17–28 (2008)

    Article  Google Scholar 

  52. 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. 55(4), 609–619 (2017)

    Article  Google Scholar 

  53. Maurice, P., et al.: Human movement and ergonomics: an industry-oriented dataset for collaborative robotics. Int. J. Robot. Res. 38(14), 1529–1537 (2019)

    Article  Google Scholar 

  54. Nemec, D., Hrubos, M., Pirnik, R., Janota, A., Simak, V.: Ergonomic remote control of the mobile platform by inertial measurement of the hand movement. In: 2016 ELEKTRO, Strbske Pleso Slovakia, pp. 445–449. IEEE (2016)

    Google Scholar 

  55. Waters, T.R., Putz-Anderson, V., Garg, A., Fine, L.J.: Revised NIOSH equation for the design and evaluation of manual lifting tasks. Ergonomics 36(7), 749–776 (1993)

    Article  Google Scholar 

  56. Waters, T.R., Putz-Anderson, V., Garg, A.: Applications Manual for the Revised NIOSH Lifting Equation. U. S. Department of Health and Human Services, National Institute for Occupational Safety and Health, Cincinnati OH (1994)

    Google Scholar 

  57. Aoki, T., Feng-Shun Lin, J., Kulic, D., Venture, G.: Segmentation of human upper body movement using multiple IMU sensors. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando FL, USA, pp. 3163–3166. IEEE (2016)

    Google Scholar 

  58. Fang, Z., Yang, Z., Wang, R.-B., Chen, S.-Y.: Inertial sensor-based knee angle estimation for gait analysis using the ant colony algorithm to find the optimal parameters for Kalman filter. In: 2018 3rd International Conference on Automation, Mechanical and Electrical Engineering (AMEE 2018), pp. 249–254 (2018)

    Google Scholar 

  59. Seel, T., Raisch, J., Schauer, T.: IMU-Based joint angle measurement for gait analysis. Sensors 14(4), 6891–6909 (2014)

    Article  Google Scholar 

  60. Roetenberg, D., Luinge, H., Slycke, P.: Xsens MVN: full 6 DOF human motion tracking using miniature inertial sensors. Xsens Motion Technologies BV, Enschede, The Netherlands (2009)

    Google Scholar 

  61. Wouda, F.J., Giuberti, M., Bellusci, G., Veltink, P.H.: Estimation of full-body poses using only five inertial sensors: an eager or lazy learning approach. Sensors 16(2), 2138 (2016)

    Article  Google Scholar 

  62. Spriggs, E.H., De La Torre, F., Hebert, M.: Temporal segmentation and activity classification from first-person sensing. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Miami FL, USA, pp. 17–24. IEEE (2009)

    Google Scholar 

  63. Bulling, A., Blanke, U., Schiele, B.: A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput. Surv. (CSUR) 46(3), 1–33 (2014)

    Article  Google Scholar 

  64. Moncada-Torres, A., Leuenberger, K., Gonzenbach, R., Luft, A., Gassert, R.: Activity classification based on inertial and barometric pressure sensors at different anatomical locations. Physiol. Meas. 35(7), 1245 (2014)

    Article  Google Scholar 

  65. Attal, F., Mohammed, S., Dedabrishvili, M., Chamroukhi, F., Oukhellou, L., Amirat, Y.: Physical human activity recognition using wearable sensors. Sensors 15(12), 31314–31338 (2015)

    Article  Google Scholar 

  66. Liaw, A., Wiener, M.: Classification and regression by randomForest. R News 2(3), 18–22 (2002)

    Google Scholar 

  67. Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation (2011)

    Google Scholar 

  68. Foxlin, E.: Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter. In: Proceedings of the IEEE 1996 Virtual Reality Annual International Symposium, Santa Clara, CA, pp. 185–194. IEEE (1996)

    Google Scholar 

  69. You, S., Neumann, U.: Fusion of vision and gyro tracking for robust augmented reality registration. In: Proceedings IEEE Virtual Reality 2001, Yokohama, Japan, pp. 71–78. IEEE (2001)

    Google Scholar 

  70. Lee, H.J., Jung, S.: Gyro sensor drift compensation by Kalman filter to control a mobile inverted pendulum robot system. In: 2009 IEEE International Conference on Industrial Technology, Gippsland, VIC, Australia, pp. 1–6. IEEE (2009)

    Google Scholar 

  71. Rigatos, G., Tzafestas, S.: Extended Kalman filtering for fuzzy modelling and multi-sensor fusion. Math. Comput. Model. Dyn. Syst. 13(3), 251–266 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  72. Lu, M.-L., Waters, T., Werren, D., Piacitelli, L.: Human posture simulation to assess cumulative spinal load due to manual lifting. Part II: accuracy and precision. Theor. Issues Ergon. Sci. 12(2), 189–203 (2011)

    Article  Google Scholar 

  73. Faber, G.S., Kingma, I., Bruijin, S.M., van Dieen, J.H.: Optimal inertial sensor location for ambulatory measurement of trunk inclination. J. Biomech. 42(14), 2406–2409 (2009)

    Article  Google Scholar 

Download references

Acknowledgements and Disclaimer

The study was financially supported by intramural funding from the National Institute for Occupational Safety and Health (NIOSH). No conflict of interest is declared. This project was supported in part by an appointment to the Research Participation Program at the Centers for Disease Control and Prevention administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and the Centers for Disease Control and Prevention. Disclaimer: The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention.

Author information

Authors and Affiliations

  1. National Institute for Occupational Safety and Health, Cincinnati, OH, USA

    Ming-Lun Lu, Menekse S. Barim, Marie Hayden & Dwight Werren

  2. FocusMotion, Focus Ventures Inc., Santa Monica, CA, USA

    Shuo Feng & Grant Hughes

Authors
  1. Ming-Lun Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Menekse S. Barim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuo Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Grant Hughes
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marie Hayden
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dwight Werren
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ming-Lun Lu .

Editor information

Editors and Affiliations

  1. Purdue University, West Lafayette, IN, USA

    Vincent G. Duffy

Rights and permissions

Reprints and permissions

Copyright information

© 2020 This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lu, ML., Barim, M.S., Feng, S., Hughes, G., Hayden, M., Werren, D. (2020). Development of a Wearable IMU System for Automatically Assessing Lifting Risk Factors. In: Duffy, V. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Posture, Motion and Health. HCII 2020. Lecture Notes in Computer Science(), vol 12198. Springer, Cham. https://doi.org/10.1007/978-3-030-49904-4_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-49904-4_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-49903-7

  • Online ISBN: 978-3-030-49904-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation