Development of Artificial Neural Networks Model to Determine Labor Rest Period Based on Environmental Ergonomics
Open accessAbstract
Food SMEs (Small and Medium Enterprises) were examples of labor-intensive industry, which involved laborers in pursuing production activities. Food SMEs require complex processes in production activities. Support to increase work productivity and
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Environmental factors such as temperature, noise, and lighting conditions are determinants in fatigue and stress scales.
“Factores como la temperatura, el ruido y las condiciones lumínicas son considerados determinantes en las escalas de fatiga y estrés [18], [19], [20], [21].”
From Impact of ergonomics on work performance in the manufacturing industry: A systematic review (2025) · cites this paper as [18]
The Introduction (page not available in our copy) says high-temperature work can cause muscle fatigue and heat stress. Section 2.1 identifies temperature, humidity, and wind velocity as workstation-environment parameters, but the paper does not establish noise or lighting as determinants or discuss fatigue and stress scales. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Li, X., Chow, K. H., Zhu, Y., & Lin, Y. (2016) · Building and Environment · doi:10.1016/j.buildenv.2015.09.005 · Cited by 211 · see citing works
Flagged first, then by citations
Research works printed but not linked: 20
- Argatov, I.I., Chai, Y.S., 2019. An Artificial Neural Network Supported Regression Model for Wear Rate. Tribology International, Volume 138, pp. 211-214
- Batubara, H., Dharmastiti, R. 2017. Redesign of Liquid Aluminum Pouring Tool Based on Participatory Ergonomics to Improve Productivity, Workload, and Musculoskeletal Disorders. International Journal of Technology, Volume 8(2), pp. 352-361
- Bolghanabadi, S., Ganjali, A., Ghalehaskar, S., 2019. Investigation of Thermal Exposure in Traditional Neyshabur Bakeries Using Heat Strain and Physiological Indices. MethodsX, Volume 6, pp. 355-359
- Cavalieri, S., Maccarrone, P., Pinto, R., 2004. Parametric Vs. Neural Network Models for The Estimation of Production Costs: A Case Study in The Automotive Industry. International Journal of Production Economics, Volume 91, pp. 165-177
- Chen, C., Xie, Y., 2014. The Impacts of Multiple Rest-Break Periods on Commercial Truck Driver's Crash Risk. Journal of safety research journal, Volume 48, pp. 87-93
- Chowdhury, S., Hamada, Y., Ahmed, K.S., 2017. Prediction and Comparison of Monthly Indoor Heat Stress (WBGT and PHS) for RMG Production Spaces in Dhaka, Bangladesh. Sustainable Cities and Society, Volume 29, pp. 41-57
- Dababneh, A.J., Swanson, N., Shell, R.L., 2001. Impact of Added Rest Breaks on The Productivity and Well Being of Workers. Ergonomics, Volume 44(2), pp. 164-174
- Darvishi, E., Khotanlou, H., Khoubi, J., Giahi, O., Mahdavi, N., 2017. Prediction Effects of Personal, Psychosocial, and Occupational Risk Factors on Low Back Pain Severity Using Artificial Neural Networks Approach in Industrial Workers. Journal of Manipulative and Physiological Therapeutics, Volume 40(7), pp. 486-493 1028 Development of Artificial Neural Networks Model to Determine Labor Rest Period Based on Environmental Ergonomics
- Di-Pasquale, V., Fruggiero, F., Iannone, R., Miranda, S., 2017. A Model for Break Scheduling Assessment in Manufacturing Systems. Computers & Industrial Engineering, Volume 111, pp. 563-580
- Fahed, A.K., Ozkaymak, M., Ahmed, S., 2018. Impacts of Heat Exposure on Workers' Health and Performance at Steel Plant in Turkey. Engineering Science and Technology, an International Journal, Volume 21, pp. 745-752
- Hami, N., Yamin, F.M., Shafie, S.M., Muhamad, M.R., Ebrahim, Z., 2018. Sustainable Manufacturing Practices Among SMEs in Malaysia. International Journal of Technology, Volume 9(8), pp. 1658-1667 ILO, 2019. Rest Periods: Definitions and Dimensions. International Labour Organization
- Janczak, A., 2005. Identification of Nonlinear Systems Using Neural Networks and Polynomial Models. New York: Springer Berlin Heidelberg New York
- Miller, V.S., Bates, G.P., 2007. The Thermal Work Limit is a Simple Reliable Heat Index for The Protection of Workers in Thermally Stressful Environments. Annals of occupational hygiene, Volume 51(6), pp. 553-561
- Negreiros, A.W.F.T.R.W. de, Silva, P.R. da, Arezes, P.M.F.M., Dangelino, R., Padula, R.S., 2019. Manufacturing Assembly Serial and Cells Layouts Impact on Rest Breaks and Workers' Health. International Journal of Industrial Ergonomics, Volume 70, pp. 22-27
- Tiacci, L., 2018. The Problem of Assigning Rest Times to Reduce Physical Ergonomic Risk At Assembly Lines. IFAC-PapersOnLine, Volume 51(11), pp. 692-697
- Ushada, M., Okayama, T., 2018. Development of Green-Affective Work System for Food SMEs. KnE Life Science, Volume 2017, pp. 89-95
- Ushada, M., Okayama, T., Khuriyati, N., Suyantohadi, A., 2017. Affective Temperature Control in Food Smes Using Artificial Neural Network. Applied Artificial Intelligence, Volume 31, pp. 555-567
- Vinoth, B., Rajarathian, A., Baumgartner, A., Bargavi, S.K.M., 2016. Nonlinear Regression and Artificial Neural Network Based Model for Forecasting Paddy (Oryzasativa) Production in Tamil Nadu. IOSR Journal of Mobile Computing & Application (IOSR-JMCA), Volume 3(3), 1-6
- Widyanti, A., Sofiani, N.F., Soetisna, H.R., Muslim, K., 2017. Eye Blink Rate as a Measure of Mental Workload in a Driving Task: Convergent or Divergent with other Measures. International Journal of Technology, Volume 8(2), pp. 283-291
- Yi, W., Chan, A.P.C., 2013. Optimizing Work-Rest Schedule for Construction Rebar Workers in Hot and Humid Environment. Building and Environment, Volume 61, pp. 104-113
Cited by
Sorted by citations
Impact of ergonomics on work performance in the manufacturing industry: A systematic review
Campos-Naval, J. E., Castañeda-López, V. A., & Aparicio-Ballena, J. A. (2025) · Proceedings of the 23rd LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI): "Engineering, Artificial Intelligence, and Sustainable Technologies in service of society" · doi:10.18687/laccei2025.1.1.802
- Authors
- Amalia, R., Ushada, M., & Pamungkas, A. P.
- Affiliations
- Universitas Gadjah Mada (ID)
- Corresponding author
- Amalia, Rosa
- Venue
- International Journal of Technology
- Publisher
- International Journal of Technology
- Published
- 2023-07-25
- Volume
- 14
- Issue
- 5
- Pages
- 1019
- ISSN
- 2087-2100 (ISSN-L)
- 2086-9614
- OpenAlex
- W4385345472
- Cited by
- 3
- References
- 1
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 1
- FWCI
- 1.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Technology (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Kementerian Riset, Teknologi dan Pendidikan Tinggi
- Universitas Gadjah Mada (DITLIT/DIT-LIT/LT/2019)
- Topics
- Health Sciences › Health Professions › Occupational Therapy › Occupational Health and Safety Management
- Physical Sciences › Engineering › Biomedical Engineering › Ergonomics and Human Factors
- Health Sciences › Health Professions › Occupational Therapy › Occupational Health and Safety in Workplaces
- Keywords
- environmental ergonomics, artificial neural networks, labor-intensive industries, labor productivity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
