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

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


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.


Wearable sensors Physical fatigue Fatigue measurement Individualized regression models 


  1. 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)CrossRefGoogle Scholar
  2. 2.
    Bureau of Labor Statistics.: Nonfatal Occupational Injuries and Illness Requiring Days Away From Work (2015).
  3. 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)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 6.
    David, G.C.: Ergonomic methods for assessing exposure to risk factors for work-related musculoskeletal disorders. Occup. Med. 55, 190–199 (2005)CrossRefGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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)CrossRefGoogle Scholar
  10. 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. 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. 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)CrossRefGoogle Scholar
  13. 13.
    Borg, G.: Borg’s Perceived Exertion and Pain Scales. Human Kinetics, Champaign (1998)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Industrial EngineeringTsinghua UniversityBeijingChina
  2. 2.Laboratory for Machine Tools and Production EngineeringRWTH Aachen UniversityAachenGermany

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