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Contemporary Low-Cost Hardware for Ergonomic Evaluation: Needs, Applications and Limitations

  • Märt Reinvee
  • Beata Mrugalska
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 793)

Abstract

This paper focuses specifically on the hardware that could be used to assist in the prevention of upper-limb musculoskeletal disorders in the working environment. It lists the types of sensors that can be used to construct application for the measurement of force excretion, posture and repetitive movements. The paper presents the criteria to evaluate such applications, discusses the intrinsic properties of the applications, highlights the major restrictions to utilize the applications and proposes further needs of development, which are necessary to improve the quality of low-cost hardware assisted ergonomic evaluations.

Keywords

Human factors Human-systems integration Work-related musculoskeletal disorders 

References

  1. 1.
    Bevan, S.: Economic impact of musculoskeletal disorders (MSDs) on work in Europe. Best Pract. Res. Clin. Rheumatol. 29, 356–373 (2015)CrossRefGoogle Scholar
  2. 2.
    European Agency for Safety and Health Work: European Risk Observatory Report. OSH in Figures: Work-related Musculoskeletal Disorders in the EU – Facts and Figures. Publications Office of the European Union, Luxemburg (2010). https://osha.europa.eu/en/tools-and-publications/publications/reports/TERO09009ENC
  3. 3.
    Bernal, D., Campos-Serna, J., Tobias, A., Vargas-Prada, S., Benavides, F.G., Serra, C.: Work-related psychosocial risk factors and musculoskeletal disorders in hospital nurses and nursing aides: a systematic review and meta-analysis. Int. J. Nurs. Stud. 52, 635–648 (2015)CrossRefGoogle Scholar
  4. 4.
    Punnett, L., Wegman, D.: Work-related musculoskeletal disorders: the epidemiologic evidence and the debate. J. Electromyogr. Kinesiol. 14(1), 13–23 (2004)CrossRefGoogle Scholar
  5. 5.
    European Agency for Safety and Health at Work: Introduccion a los trastornos musculoesquele´ ticos de origen laboral. FACTS 71 (es), pp. 1681–2085 (2007). https://osha.europa.eu/es/publications/factsheets/71
  6. 6.
    Lang, J., Ochsmann, E., Kraus, T., Lang, J.W.B.: Psychosocial work stressors as antecedents of musculoskeletal problems: a systematic review and meta-analysis of stability-adjusted longitudinal studies. Soc. Sci. Med. 75, 1163–1174 (2012)CrossRefGoogle Scholar
  7. 7.
    Hartvigsen, J., Lings, S., Leboeuf-Yde, C., Bakketeig, L.: Psychosocial factors at work in relation to low back pain and consequences of low back pain; a systematic, critical review of prospective cohort studies. Occup. Environ. Med. 61, 2 (2004)Google Scholar
  8. 8.
    Chiasson, M.-E., Imbeau, D., Major, J., Aubry, K., Delisle, A.: Influence of musculoskeletal pain on workers’ ergonomic risk-factor assessments. Appl. Ergon. 49, 1–7 (2015)CrossRefGoogle Scholar
  9. 9.
    Hembecker, P.K., Reis, D.C., Konrath, A.C., Gontijo, L.A., Merino, E.A.D.: Investigation of musculoskeletal symptoms in a manufacturing company in Brazil: a cross-sectional study. Braz. J. Phys. Ther. 21(3), 175–183 (2017)CrossRefGoogle Scholar
  10. 10.
    Winkel, J., Mathiassen, S.E.: Assessment of physical work load in epidemiologic studies: concepts, issues and operational considerations. Ergonomics 37, 979–988 (1994)CrossRefGoogle Scholar
  11. 11.
    Takala, E.P., Pehkonen, I., Forsman, M., Hansson, G.Å., Mathiassen, S.E., Neumann, W.P., Sjøgaard, G., Veiersted, K.B., Westgaard, R.H., Winkel, J.: Systematic evaluation of observational methods assessing biomechanical exposures at work. Scand. J. Work. Environ. Heal. 36, 3–24 (2010)CrossRefGoogle Scholar
  12. 12.
    Chiasson, M.-E., Imbeau, D., Aubry, K., Delisle, A.: Comparing the results of eight methods used to evaluate risk factors associated with musculoskeletal disorders. Int. J. Ind. Ergon. 42, 478–488 (2012)CrossRefGoogle Scholar
  13. 13.
    Kjellberg, K., Lindberg, P., Nyman, T., Palm, P., Rhen, I.-M., Eliasson, K., Carlsson, R., Balliu, N., Forsman, M.: Comparisons of six observational methods for risk assessment of repetitive work - results from consensus assessment. In: Proceedings of 19th Triennial Congress of the IEA, Melbourne, Australia (2015)Google Scholar
  14. 14.
    Laurig, W., Rombach, V.: Expert systems in ergonomics: requirements and an approach. Ergonomics 32, 795–811 (1989)CrossRefGoogle Scholar
  15. 15.
    Schall, M.C., Sesek, R.F., Cavuoto, L.A.: Barriers to the adoption of wearable sensors in the workplace: a survey of occupational safety and health professionals. Hum. Factors J. Hum. Factors Ergon. Soc. 60, 351–362 (2018)CrossRefGoogle Scholar
  16. 16.
    D’Ausilio, A.: Arduino: a low-cost multipurpose lab equipment. Behav. Res. Methods 44, 305–313 (2012)CrossRefGoogle Scholar
  17. 17.
    Guerreiro, J., Lourenço, A., Silva, H., Fred, A.: Performance comparison of low-cost hardware platforms targeting physiological computing applications. Procedia Technol. 17, 399–406 (2014)CrossRefGoogle Scholar
  18. 18.
    Schreuders, T.A.R., Selles, R.W., Roebroeck, M.E., Stam, H.J.: Strength measurements of the intrinsic hand muscles: a review of the development and evaluation of the rotterdam intrinsic hand myometer. J. Hand Ther. 19, 393–402 (2006)CrossRefGoogle Scholar
  19. 19.
    Rozmaryn, L.M., Bartko, J.J., Isler, M.L.D.: The ab-adductometer: a new device for measuring the muscle strength and function of the thumb. J. Hand Ther. 20, 311–325 (2007)CrossRefGoogle Scholar
  20. 20.
    Örtengren, R., Cederqvist, T., Lindberg, M., Magnusson, B.: Workload in lower arm and shoulder when using manual and powered screwdrivers at different working heights. Int. J. Ind. Ergon. 8, 225–235 (1991)CrossRefGoogle Scholar
  21. 21.
    Lee, S.J., Kong, Y.-K., Lowe, B.D., Song, S.: Handle grip span for optimising finger-specific force capability as a function of hand size. Ergonomics 52, 601–608 (2009)CrossRefGoogle Scholar
  22. 22.
    Reinvee, M., Jansen, K.: Utilisation of tactile sensors in ergonomic assessment of hand-handle interface: a review. Agron. Res. 12, 907–914 (2014)Google Scholar
  23. 23.
    Pylatiuk, C., Kargov, A., Schulz, S., Döderlein, L.: Distribution of grip force in three different functional prehension patterns. J. Med. Eng. Technol. 30, 176–182 (2006)CrossRefGoogle Scholar
  24. 24.
    Castro, M.C., Cliquet, A.: A low-cost instrumented glove for monitoring forces during object manipulation. IEEE Trans. Rehabil. Eng. 5, 140–147 (1997)CrossRefGoogle Scholar
  25. 25.
    Fellows, G.L., Freivalds, A.: The use of force sensing resistors in ergonomic tool design. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 33, 713–717 (1989)CrossRefGoogle Scholar
  26. 26.
    Lowe, B.D., Kong, Y.-K., Han, J.: Development and application of a hand force measurement system. In: Pikaar, R.N., Koningsveld, E.A.P., Settels, P.J.M. (eds.) Proceedings of the XVIth Triennial Congress of the International Ergonomics Association. IEA, Maastricht (2006)Google Scholar
  27. 27.
    Reinvee, M., Vaas, P., Ereline, J., Pääsuke, M.: Applicability of affordable sEMG in ergonomics practice. Procedia Manuf. 3, 4260–4265 (2015)CrossRefGoogle Scholar
  28. 28.
    Reinvee, M., Pääsuke, M.: Overview of contemporary low-cost sEMG hardware for applications in human factors and ergonomics. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 60, 408–412 (2016)CrossRefGoogle Scholar
  29. 29.
    Jonsson, B.: Measurement and evaluation of local muscular strain in the shoulder during constrained work. J. Hum. Ergol. 11, 73–88 (1982)Google Scholar
  30. 30.
    Hansson, G.Å., Asterland, P., Holmer, N.G., Skerfving, S.: Validity and reliability of triaxial accelerometers for inclinometry in posture analysis. Med. Biol. Eng. Comput. 39, 405–413 (2001)CrossRefGoogle Scholar
  31. 31.
    Bernmark, E., Wiktorin, C.: A triaxial accelerometer for measuring arm movements. Appl. Ergon. 33, 541–547 (2002)CrossRefGoogle Scholar
  32. 32.
    Lugade, V., Fortune, E., Morrow, M., Kaufman, K.: Validity of using tri-axial accelerometers to measure human movement—part I: posture and movement detection. Med. Eng. Phys. 36, 169–176 (2014)CrossRefGoogle Scholar
  33. 33.
    Nez, A., Fradet, L., Laguillaumie, P., Monnet, T., Lacouture, P.: Comparison of calibration methods for accelerometers used in human motion analysis. Med. Eng. Phys. 38, 1289–1299 (2016)CrossRefGoogle Scholar
  34. 34.
    Holtermann, A., Schellewald, V., Mathiassen, S.E., Gupta, N., Pinder, A., Punakallio, A., Veiersted, K.B., Weber, B., Takala, E.P., Draicchio, F., Enquist, H., Desbrosses, K., García Sanz, M.P., Malińska, M., Villar, M., Wichtl, M., Strebl, M., Forsman, M., Lusa, S., Tokarski, T., Hendriksen, P., Ellegast, R.: A practical guidance for assessments of sedentary behavior at work: a PEROSH initiative. Appl. Ergon. 63, 41–52 (2017)CrossRefGoogle Scholar
  35. 35.
    Yang, L., Grooten, W.J.A., Forsman, M.: An iPhone application for upper arm posture and movement measurements. Appl. Ergon. 65, 492–500 (2017)CrossRefGoogle Scholar
  36. 36.
    Dutta, T.: Evaluation of the Kinect sensor for 3-D kinematic measurement in the workplace. Appl. Ergon. 43, 645–649 (2012)CrossRefGoogle Scholar
  37. 37.
    Diego-Mas, J.A., Alcaide-Marzal, J.: Using KinectTM sensor in observational methods for assessing postures at work. Appl. Ergon. 45, 976–985 (2014)CrossRefGoogle Scholar
  38. 38.
    Patrizi, A., Pennestrì, E., Valentini, P.P.: Comparison between low-cost marker-less and high-end marker-based motion capture systems for the computer-aided assessment of working ergonomics. Ergonomics 139, 1–11 (2015)Google Scholar
  39. 39.
    Manghisi, V.M., Uva, A.E., Fiorentino, M., Bevilacqua, V., Trotta, G.F., Monno, G.: Real time RULA assessment using Kinect v2 sensor. Appl. Ergon. 65, 481–491 (2017)CrossRefGoogle Scholar
  40. 40.
    Geiselhart, F., Otto, M., Rukzio, E.: On the use of multi-depth-camera based motion tracking systems in production planning environments. Procedia CIRP 41, 759–764 (2016)CrossRefGoogle Scholar
  41. 41.
    Cavanagh, P.R.: Electromyography: its use and misuse in physical education. J. Heal. Phys. Educ. Recreat. 45, 61–64 (1974)CrossRefGoogle Scholar
  42. 42.
    De Luca, C.J.: The use of surface electromyography in biomechanics. J. Appl. Biomech. 13, 135–163 (1997)CrossRefGoogle Scholar
  43. 43.
    International Ergonomics Association: Summary of core competencies in ergonomics: Units and elements of competency. Version 2 (2001)Google Scholar
  44. 44.
    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, 314–326 (2013)CrossRefGoogle Scholar
  45. 45.
    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, 566–574 (2013)CrossRefGoogle Scholar
  46. 46.
    Peppoloni, L., Filippeschi, A., Ruffaldi, E., Avizzano, C.A.: A novel wearable system for the online assessment of risk for biomechanical load in repetitive efforts. Int. J. Ind. Ergon. 52, 1–11 (2016)CrossRefGoogle Scholar
  47. 47.
    Mohammadzadeh, F.F., Liu, S., Bond, K.A., Nam, C.S.: Feasibility of a wearable, sensor-based motion tracking system. Procedia Manuf. 3, 192–199 (2015)CrossRefGoogle Scholar
  48. 48.
    Diego-Mas, J.-A., Alcaide-Marzal, J., Poveda-Bautista, R.: Errors using observational methods for ergonomics assessment in real practice. Hum. Factors 59, 1173–1187 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of TechnologyEstonian University of Life SciencesTartuEstonia
  2. 2.Faculty of Engineering ManagementPoznan University of TechnologyPoznanPoland

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