Affiliations: [a] Construction Management Program, Michigan State University, East Lansing, MI, USA | [b] Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, USA
Correspondence:
[*]
Address for correspondence: Dr. Tariq Abdelhamid, 207 Farrall Hall, Construction Management Program, Michigan State University, East Lansing, MI 48824-1323, USA. Tel.: +1 517 432 6188; Fax: +1 517 355 7711; E-mail: [email protected]
Abstract: Expressing absolute workload as a percentage of maximum oxygen uptake (VO2max), commonly known as relative workload, is recommended by many work physiologists because it provides a subject-specific workload and enables accurate assessment of physical fatigue. The specific aim of this research is to develop a direct method to predict relative workload from in-situ collected sub-maximal oxygen uptake data without the need to determine maximum oxygen uptake. The method is developed based on a hypothesis that oxygen uptake data are serially dependent, and that by using data dependent systems (DDS) modeling and time series analysis techniques a regression model between relative workload and a statistical characteristic of collected oxygen uptake data can be developed. The technique was developed using twenty subjects and validated on five. The estimated standard error of prediction using the developed technique for relative workload (%VO2max) is ± 3.4% and maximum oxygen uptake (VO2max) is ± 0.5 litre · min-1. Validation subjects' results indicated that the mean square error of the regression model is not seriously biased and gives an appropriate indication of the predictive capability of the selected regression model. With further development, the technique presented will be valuable in identifying excessively demanding tasks based on a more subject-specific workload.
Keywords: Physical workload, physical fatigue, time series analysis, DDS techniques
