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A Work-related Musculoskeletal Disorder Risk Assessment Platform using Smart Sensor

스마트센서를 활용한 근골격계 질환 위험 평가 플랫폼

  • Loh, Byoung Gook (Department of Mechanical Systems Engineering, Hansung University)
  • 노병국 (한성대학교 기계시스템공학과)
  • Received : 2015.04.20
  • Accepted : 2015.05.28
  • Published : 2015.06.30

Abstract

Economic burden of work-related musculoskeletal disorder(WMDs) is increasing. Known causes of WMDs include improper posture, repetition, load, and temperature of workplace. Among them, improper postures play an important role. A smart sensor called SensorTag is employed to estimate the trunk postures including flexion-extension, lateral bend, and the trunk rotational speeds. Measuring gravitational acceleration vector in the smart sensor along the tri-orthogonal axes offers an orientation of the object with the smart sensor attached to. The smart sensor is light in weight and has small form factor, making it an ideal wearable sensor for body posture measurement. Measured data from the smart senor is wirelessly transferred for analysis to a smartphone which has enough computing power, data storage and internet-connectivity, removing need for additional hardware for data post-processing. Based on the estimated body postures, WMDs risks can be conviently gauged by using existing WMDs risk assesment methods such as OWAS, RULA, REBA, etc.

Keywords

References

  1. B. Buchholz, V. Paquet, L. Punnett, D. Lee and S. Moir, "PATH: A Work Sampling-based Approach to Ergonomic Job Analysis for Construction and Other non-repetitive Work", Applied Ergonomics, Vol. 27, pp. 177-187, 1996. https://doi.org/10.1016/0003-6870(95)00078-X
  2. K. Kim, "Analysis of Musculoskeletal Diseases Compensation Status", Korea Workers' Compenstation & Welfare Service, Vol. Ra, pp. 2-4, 2012.
  3. J. T. Spector, M. Lieblich, S. Bao, K. McQuade and M. Hughes, "Automation of Workplace Lifting Hazard Assessment for Musculoskeletal Injury Prevention", Annals of Occupational and Environmental medicine, Vol. 26, pp. 15, 2014. https://doi.org/10.1186/2052-4374-26-15
  4. J. A. Diego-Mas and J. Alcaide-Marzal, "Using Kinect™ Sensor in Observational Methods for Assessing Postures at Work", Applied Ergonomics, Vol. 45, pp. 976-985, 2014. https://doi.org/10.1016/j.apergo.2013.12.001
  5. T. Dutta, "Evaluation of the KinectTM Sensor for 3-D Kinematic Measurement in the Workplace", Applied Ergonomics, Vol. 43, pp. 645-649, 2012. https://doi.org/10.1016/j.apergo.2011.09.011
  6. C. A. Sutherland, W. J. Albert, A. T. Wrigley and J. P. Callaghan, "A Validation of a Posture Matching Approach for the Determination of 3D Cumulative Back Loads", Applied Ergonomics, Vol. 39, pp. 199-208, 2008. https://doi.org/10.1016/j.apergo.2007.05.004
  7. F. Abyarjoo, A. Barreto, J. Cofino and F. R. Ortega, "Implementing a Sensor Fusion Algorithm for 3D Orientation Detection with Inertial/Magnetic Sensors", in Innovations and Advances in Computing, Informatics, Systems Sciences, Networking and Engineering, ed: Springer, pp. 305-310, 2015.
  8. A. M. Sabatini, "Quaternion-based Extended Kalman Filter for Determining Orientation by Inertial and Magnetic Sensing", Biomedical Engineering, IEEE Transactions On, Vol. 53, pp. 1346-1356, 2006. https://doi.org/10.1109/TBME.2006.875664
  9. N. Saeed, Introduction to Robotics: Prentice Hall, 2001.
  10. H. Robin, Bluetooth Low Energy: The Developer's Handbook, 2012.
  11. O. Karhu, R. Harkonen, P. Sorvali and P. Vepsalainen, "Observing Working Postures in Industry: Examples of OWAS Application", Applied Applied Ergonomics, Vol. 12, No.1, pp. 13-7, 1981. https://doi.org/10.1016/0003-6870(81)90088-0
  12. L. McAtamney and E. Nigel Corlett, "RULA: a survey method for the investigation of work-related upper limb disorders", Applied ergonomics, vol. 24, pp. 91-99, 1993. https://doi.org/10.1016/0003-6870(93)90080-S
  13. S. Hignett and L. McAtamney, "Rapid Entire Body Assessment (REBA)", Applied Ergonomics, Vol. 31, pp. 201-205, 2000. https://doi.org/10.1016/S0003-6870(99)00039-3