Estimation of Energy Expenditure Based on Divided Manual Material Handling Task: Waste Collection Example

The objectives of this study were: i) to present an approach for potential areas of ergonomic analysis software usage and to get familiar with the usage of this kind of problem assessment by applying the software to a practical example in a problem area with significant ergonomic problems to be analyzed. ii) to estimate energy expenditure rates for waste collection task using the University of Michigan Energy Expenditure Prediction Program (EEPP) to comfort worker safety and health. iii) to compare with regression equations in literature to illustrate the performance of the EEPP software. The assessment will make use of a University of Michigan EEPP to predict energy expenditure of materials handling tasks to comfort worker safety and health. A manual waste collection task was selected as a job analysis and the results will be compared with NIOSH guidelines and regression equations in literature to illustrate the performance of the EEPP software. The results show that EEPP software and prediction equation estimated quite close average task energy rate (kcal/min). This predicted information can be useful for waste collection job design instead of using oxygen consumption measurement which takes long time and costly. Furthermore, these results can be used for recommendations to improve ergonomic factors of the waste collection tasks in form of a guideline

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[1] Center for Sustainable Systems, University of Michigan. “Municipal Solid Waste Factsheet.” Pub. No. CSS04-15, 2011
[2] Çakıt, E. Assessment of The Physıcal Demands of Waste Collectıon Tasks. Global NEST Journal. 2015; 17(2), 426-438.
[3] Kuijer, P.; Paul, F.M.; Judith, K.; Sluiter, J.K.; Monique H.W; Frings-Dresen, M.H.W. Health and Safety in Waste Collection: Towards Evidence-Based Worker Health Surveillance. American Journal of Industrial Medicine. 2010; 53, 1040–1064.
[4] An, H; Englehardt, J.; Fleming, L.; Bean, J. Occupational health and safety amongst municipal solid waste workers in Florida. Waste Manage Res. 1999; 17, 369–377.
[5] Garg, A.; Chaffin, D.B.; Herrin, G.D. Prediction of metabolic rates for manual handling jobs. American Industrial Hygiene Association Journal. 1978; 39, 661–674.
[6] Dempsey, P.G.; Ciriello, V.M.; Maikala, R.V.; O'Brien, N.V. Oxygen consumption prediction models for individual and combination materials handling tasks. Ergonomics. 2008; (51)11, 1776-1789.
[7] Levine, J.A. Measurement of energy expenditure. Public Health Nutrition. 2005; 8,pp 1123-1132.
[8] Assah, F. K.; Ekelund, U.; Brage, S.; Wright, A;, Mbanya, J. C.; Wareham, N. J. Accuracy and validity of a combined heart rate and motion sensor for the measurement of freeliving physical activity energy expenditure in adults in Cameroon. International Journal of Epidemiology. 2010; 40(1), 112-120.
[9] Crouter, S.E.; Clowers, K.G.; Bassett, D.R Jr. A novel method for using accelerometer data to predict energy expenditure. J Appl Physiol. 2006; 100, 1324–1331.
[10] Heil, D.P. Predicting activity energy expenditure using the actical activity monitor. Res Q Exerc Sport. 2006; 77, 64– 80.
[11] Zakeri, I.F.; Adolph, A.L.; Puyau, M.R.; Vohra, F.A.; Butte, N.F. Multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents. Journal of Applied Physiology. 2010; 108(1), 128-136.
[12] Hay, D.C.; Wakayama, A.; Sakamura, K.; Fukashiro, S. Improved estimation of energy expenditure by artificial neural network modeling. Appl Physiol Nutr Metab. 2010; 33(6), 1213-22.
[13 Anon. Energy Expenditure Prediction Program.] University of Michigan, http://www.engin.umich.edu/dept/ioe/ENGEXP/index.html
[14] Chaffin, D.B. Some effects of Physical Exertion. Dept. of Ind. And Operations Eng, The university of Michigan 1972.