Original Article

A Data Driven Approach for Optimization of Rest Allowances


Despite all technological improvement and automation in the production process, a majority of tasks are still performing by workers. Due to this challenge, the occurrence of musculoskeletal disorders (MSDs) is an expected common result of manual tasks. Fatigue is one of the common causes resulting MSDs. Hence, one of the strategies for resolving this issue is to schedule rest time to provide a recovery time for workforce from the physiological consequences of exertion. This study was aimed to suggest a pre-planned rest allowance at MAPNA Company, Tehran, Iran. Therefore, we designed an experiment in a workstation to obtain input data (postures and forces). Then, the collected data was used to simulate the working condition for all workers using 3DSSPP software. We considered maximum voluntary contraction (MVC) of involved muscles. So, the critical muscle was determined for all workers based on specific tasks. The rest time for a critical muscle of each worker was calculated using the Rohmert model. Results showed that optimally work time schedule based on the task specification and subsequent rest time could reduce MSDs. This approach provides a comprehensive view of workers and their tasks (for both sources p-value was less than 0.05). This approach can be used for any workstation to suggest pre-planned rest allowances.

1. Liang Maa, Damien Chablat, Fouad Bennis, Wei Zhang, A new simple dynamic muscle fatigue model and its validation, International Journal of Industrial Ergonomics 39 (2009) 211–220.
2. Calzavara, M., Persona, A. and Sgarbossa, F. ‘A model for rest allowance estimation to improve tasks assignment to operators’, International Journal of Production Research. Taylor & Francis, (2018) 1–15.
3. El ahrache, K. and Imbeau, D. ‘Comparison of rest allowance models for static muscular work’, International Journal of Industrial Ergonomics. Elsevier B.V., 39(1), (2009) 73–80.
4. Occupational Biomechanics (3rd ed.) Edited by Don B. Chaffin, Gunnar B. J. Andersson, & Bernard J. Martin 1999, 579.
5.Jing-Jing Wan , Zhen Qin , Peng-Yuan Wang , Yang Sun , Xia Liu, Muscle fatigue: general understanding and treatment Exp Mol Med, 2017 Oct 6;49(10):e384.
6. Strober, L. B. & Deluca, I. Fatigue: Its influence on cognition and assessment. In P. Arnett (Ed.), Secondary influences on neuropsychological test performance (2013) 117.
7. Sedighi, Z. Yazdi, M. Cavuoto, L. Megahed, F. ‘A data-driven approach to modeling physical fatigue in the workplace using wearable sensors’, Applied Ergonomics. Elsevier Ltd, 65(March) (2017). 515–529.
8. Rohmert, W. and Republic, G. F. ‘Problems in determining rest allowances’, Appl Ergon (1973) 91–95.
9. Bendak, S. and Rashid, H. S. J. ‘Fatigue in aviation: A systematic review of the literature’, International Journal of Industrial Ergonomics. Elsevier B.V., 76(February) (2020), 102928.
10 .Niebel, B.W., Freivalds, A., Methods, Standards, and Work Design, 11th ed. McGraw Hill, Boston, MA. 2003.
11. University of Michigan, [3DSSPP (v4.32)]. Michigan. 1986–2001.
12. International Organization for Standardization (ISO), Ergonomics-Evaluation of static working postures-ISO11226, Geneva. 2000.
13. Law, L. A. F. and Avin, K. G. ‘Endurance time is joint-specific: A modelling and meta-analysis investigation’, Ergonomics. . Taylor & Francis, 53(1), (2011). 109–129.
14. Kakarot, N. Mueller, F and Bassarak, C. ‘Activity–rest schedules in physically demanding work and the variation of responses with age’, Taylor & Francis, Vol. 55, No. 3 (2011). 282-94.
IssueVol 13 No 1 (2021) QRcode
SectionOriginal Article(s)
Rest allowances Optimization Experiment design Musculoskeletal disorders

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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Mirhaji A, Salmanzadeh H. A Data Driven Approach for Optimization of Rest Allowances. Int J Occup Hyg. 2021;13(1):79-90.