Skip to main content

Using Non-invasive Wearable Sensors to Estimate Perceived Fatigue Level in Manual Material Handling Tasks

  • Conference paper
  • First Online:

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

Abstract

Physical fatigue in manual material handling (MMH) may cause musculoskeletal disorders (MSDs), which threatens the well-being of workers. However, conventional techniques for measuring fatigue have their limitations and are difficult to implement in realistic working conditions without sufficient resources. In this study, we proposed a method utilizing non-invasive wearable sensors to collect bio-signals (respiration, photo-plethysmography, and electrodermal activity) and estimate perceived physical fatigue. Six participants volunteered in two MMH tasks at two paces. Subsets of five bio-signal measures were selected to estimate perceived fatigue levels using a universal regression model and six individualized regression models. We classified perceived fatigue into three levels and examined the correct classification rate of the estimated fatigue levels. Correct classification rates for the general model and the individualized models were 67% and 80%, respectively. Results confirm the feasibility to predict fatigue level using wearable sensors, but the regression models should be used with caution.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
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

Learn about institutional subscriptions

References

  1. Armstrong, T.J., Buckle, P., Fine, L.J., Hagberg, M., Jonsson, B., Kilbom, A., Kuorinka, I.A., Silverstein, B.A., Sjogaard, G., Viikari-Juntura, E.R.: A conceptual model for work-related neck and upper-limb musculoskeletal disorders. Scand. J. Work Environ. Health. 19, 73–84 (1993)

    Article  Google Scholar 

  2. Bureau of Labor Statistics.: Nonfatal Occupational Injuries and Illness Requiring Days Away From Work (2015). https://www.bls.gov/news.release/pdf/osh2.pdf

  3. Yu, W., Yu, I.T.S., Li, Z., Wang, X., Sun, T., Lin, H., Wan, S., Qiu, H., Xie, S.: Work-related injuries and musculoskeletal disorders among factory workers in a major city of China. Accid. Anal. Prev. 48, 457–463 (2012)

    Article  Google Scholar 

  4. Yu, S., Lu, M.-L., Gu, G., Zhou, W., He, L., Wang, S.: Musculoskeletal symptoms and associated risk factors in a large sample of Chinese workers in Henan province of China. Am. J. Ind. Med. 55, 281–293 (2012)

    Article  Google Scholar 

  5. Helander, M.G., Burri, G.J.: Cost effectiveness of ergonomics and quality improvements in electronics manufacturing. Int. J. Ind. Ergon. 15, 137–151 (1995)

    Article  Google Scholar 

  6. David, G.C.: Ergonomic methods for assessing exposure to risk factors for work-related musculoskeletal disorders. Occup. Med. 55, 190–199 (2005)

    Article  Google Scholar 

  7. Li, G., Buckle, P.: Current techniques for assessing physical exposure to work-related musculoskeletal risks, with emphasis on posture-based methods. Ergonomics 42, 674–695 (1999)

    Article  Google Scholar 

  8. Pantelopoulos, A., Bourbakis, N.G.: A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40, 1–12 (2010)

    Article  Google Scholar 

  9. O’Reilly, M.A., Whelan, D.F., Ward, T.E., Delahunt, E., Caulfield, B.M.: Classification of deadlift biomechanics with wearable inertial measurement units. J. Biomech. 58, 155–161 (2017)

    Article  Google Scholar 

  10. Reenalda, J., Maartens, E., Homan, L., Buurke, J.H.(Jaap): Continuous three dimensional analysis of running mechanics during a marathon by means of inertial magnetic measurement units to objectify changes in running mechanics. J. Biomech. 49, 3362–3367 (2016)

    Google Scholar 

  11. Kakria, P., Tripathi, N.K., Kitipawang, P.: A real-time health monitoring system for remote cardiac patients using smartphone and wearable sensors. Int. J. Telemed. Appl. 2015, 1–11 (2015). Article no. 8

    Google Scholar 

  12. Maman, Z.S., Yazdi, M.A.A., Cavuoto, L.A., Megahed, F.M.: A data-driven approach to modeling physical using wearable sensors. Appl. Ergon. 65, 515–529 (2017)

    Article  Google Scholar 

  13. Borg, G.: Borg’s Perceived Exertion and Pain Scales. Human Kinetics, Champaign (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Liang Ma .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tsao, L., Ma, L., Papp, CT. (2019). Using Non-invasive Wearable Sensors to Estimate Perceived Fatigue Level in Manual Material Handling Tasks. In: Ahram, T. (eds) Advances in Human Factors in Wearable Technologies and Game Design. AHFE 2018. Advances in Intelligent Systems and Computing, vol 795. Springer, Cham. https://doi.org/10.1007/978-3-319-94619-1_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-94619-1_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94618-4

  • Online ISBN: 978-3-319-94619-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics