Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.448884
Title: Measurement and modelling of postural work load.
Author: Barbonis, P. A.
Awarding Body: University of Birmingham
Current Institution: University of Birmingham
Date of Award: 1978
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Abstract:
This study was concerned with the measurement and modelling of postural work load and recovery. Five stationary stooped postures were examined and the data derived therefrom were used in the development of some predictive mathematical models of postural work recovery. The models were found to fit the data very well and, when tested in two different situations, were found to be about 70% reliable, at the worst. The models tend to refute the view that a knowledge of the forces being exerted by the various muscles is necessary before postural work recovery can be predicted. The models showed, at least for the five postures studied, the duration of work was the primary factor influencing recovery, rather than rest. Rest pauses of about 1200% of work were used for some of the subjects, despite which full and complete recovery was not achieved. The models lend support, albeit obliquely, to some studies at ~he micro-level involving muscle-biopsies, performed to elucidate the possible mechanisms which underlie the fatigue and recovery processes. In keeping with the multi-disciplinary nature of ergonomics, this study has taken an eclectic course, drawing upon the theories and methods of such areas as statistics and communication engineering to produce two new techniques for the analysis of subjective assessment data. These-two techniques make subjective assessment data more amenable to quantitative analysis, thereby affording some precision in the handling of data which are susceptible to personalistic or idiosyncratic influences. Bayes Theorem and Shannon's Information Theory have been the basis of these two techniques.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.448884  DOI: Not available
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