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Evaluating an Inertial Measurement Unit Based System for After-Reach Speed Measurement in Power Press Applications

Conference paper
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Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 795)

Abstract

The objective of this study was to measure the hand speed of eighteen (18) subjects making after-reach movements from an upper palm button (UB) and lower palm button (LB) on a simulated press. Each after-reach movement was measured with a Vicon optical motion capture system and an Xsens IMU based system. A Bland-Altman analysis of the speed measured by the two technologies demonstrated a general agreement (average bias 0.19 m/s) between the measurements and a potential for using IMUs for hand-speed measures in the future. However, the computation intensity required to manipulate the Xsens data is likely too complex and time consuming for practitioners who are busy with plant activities. A simple IMU system, designed specifically for hand speed capture, could be a viable option for measuring after-reach speed in the future.

Keywords

Hand speed Palm button Machine guarding After-reach IMU 

Notes

Acknowledgement

This publication was partially supported by Grant # 2T420H008436 from NIOSH. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. Use of trade names is for identification only and does not constitute endorsement by the Public Health Service or by the U.S. Department of Health and Human Service.

References

  1. 1.
    Baldwin, D.M.: The power press controversy- a status report. Job Saf. Health (OSHA) 4(5), 12–18 (1976)Google Scholar
  2. 2.
    NIOSH: CDC - NIOSH Publications and Products - Injuries and Amputations Resulting from Work with Mechanical Power Presses (87-107), Current Intelligence Bulletin 49 (2017)Google Scholar
  3. 3.
  4. 4.
    Lobl, O.: On the Two-Hand Insertion on Eccentric Presses-A Study of the Limits of the Protective Effect. Reichsarbeitsblatt (Berlin), 20 (part 111) (1935)Google Scholar
  5. 5.
    Pizatella, T.J., Moll, M.B.: Simulation of the after-reach hazard on power presses using dual palm button actuation. Hum. Factors 29(1), 9–18 (1987)CrossRefGoogle Scholar
  6. 6.
    Pizatella, T.J., Etherton, J.R., Jensen, R., Oppold, J.A.: Investigation of the after-reach hazard in two-hand controlled power press operations. Scand. J. Work Environ. Health 9(2), 194–200 (1983)CrossRefGoogle Scholar
  7. 7.
    Horton, J.T., Pizatella, T.J., Plummer, R.W.: The effect of palm button location on hand reach speed for power press operations. In: Trends in Ergonomics/Human Factors III. Elsevier Science Publishers B.V. (1986)Google Scholar
  8. 8.
    Jensen, R., Stobbe, T.: Safe distance for machinery actuators: is after-reach speed a constant?. In: Advances in Safety Management and Human Factors, pp. 321–331. Springer (2016)Google Scholar
  9. 9.
    Woodman, O.J.: An introduction to inertial navigation (No. Technical report 696). University of Cambridge, Computer Laboratory (2007)Google Scholar
  10. 10.
    Roetenberg, D., Luinge, H., Slycke, P.: Xsens MVN: Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors, vol. 3 (2009)Google Scholar
  11. 11.
    de Vries, W.H.K., Veeger, H.E.J., Baten, C.T.M., van der Helm, F.C.T.: Magnetic distortion in motion labs, implications for validating inertial magnetic sensors. Gait Posture 29(4), 535–541 (2009)CrossRefGoogle Scholar
  12. 12.
    Merriaux, P., Dupuis, Y., Boutteau, R., Vasseur, P., Savatier, X.: A study of Vicon system positioning performance. Sensors 17(7), 1591 (2017)CrossRefGoogle Scholar
  13. 13.
    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 9(3), e87640 (2014)CrossRefGoogle Scholar
  14. 14.
    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).  https://doi.org/10.1007/s11517-016-1537-2CrossRefGoogle Scholar
  15. 15.
    Zhou, H., Hu, H., Tao, Y.: Inertial measurements of upper limb motion. Med. Biol. Eng. Comput. 44(6), 479–487 (2006)CrossRefGoogle Scholar
  16. 16.
    Luinge, H.J., Veltink, P.H., Baten, C.T.M.: Ambulatory measurement of arm orientation. J. Biomech. 40(1), 78–85 (2007)CrossRefGoogle Scholar
  17. 17.
    Zhou, H., Hu, H.: Inertial sensors for motion detection of human upper limbs. Sens. Rev. 27(2), 151–158 (2007)CrossRefGoogle Scholar
  18. 18.
    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)CrossRefGoogle Scholar
  19. 19.
    Godwin, A., Agnew, M., Stevenson, J.: accuracy of inertial motion sensors in static, quasistatic, and complex dynamic motion. J. Biomech. Eng. 131(11), 114501 (2009)CrossRefGoogle Scholar
  20. 20.
    Saber-Sheikh, K., Bryant, E.C., Glazzard, C., Hamel, A., Lee, R.Y.W.: Feasibility of using inertial sensors to assess human movement. Man. Ther. 15(1), 122–125 (2010)CrossRefGoogle Scholar
  21. 21.
    Lebel, K., Boissy, P., Hamel, M., Duval, C.: Inertial measures of motion for clinical biomechanics: comparative assessment of accuracy under controlled conditions - effect of velocity. PLoS ONE 8(11), e79945 (2013)CrossRefGoogle Scholar
  22. 22.
    Kim, S., Nussbaum, M.A.: Performance evaluation of a wearable inertial motion capture system for capturing physical exposure during manual material handling. Ergonomics 56, 314–326 (2013)CrossRefGoogle Scholar
  23. 23.
    Schall, M.C., Fethke, N.B., Chen, H., Gerr, F.: A comparison of instrumentation methods to estimate thoracolumbar motion in field-based occupational studies. Appl. Ergon. 48, 224–231 (2015)CrossRefGoogle Scholar
  24. 24.
    Schall, 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 (2016)CrossRefGoogle Scholar
  25. 25.
    Altman, D.G., Bland, J.M.: Measurement in medicine: the analysis of method comparison studies. J. R. Stat. Soc. Ser. D (Stat.) 32(3), 307–317 (1983)Google Scholar
  26. 26.
    Bland, J.M., Altman, D.G.: Statistical methods for assessing agreement between two methods of clinical measurement. Int. J. Nurs. Stud. 47(8), 307–310 (2010)CrossRefGoogle Scholar
  27. 27.
    Zou, G.: Confidence interval estimation for the bland-altman limits of agreement with multiple observations per individual. Stat. Methods Med. Res. 22(6), 630–642 (2013)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Industrial and Systems EngineeringAuburn UniversityAuburnUSA
  2. 2.Department of Mechanical EngineeringAuburn UniversityAuburnUSA

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