Bodyspace: Anthropometry, Ergonomics and the Design of the by STEPHEN PHEASANT


This variation has been revised to carry clean insights into the rules and perform of anthropometrics, workspace layout, sitting and seating, palms and handles, ergonomics within the workplace, ergonomics in the house, and healthiness and safeguard at paintings.

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Extra info for Bodyspace: Anthropometry, Ergonomics and the Design of the Work, Second Edition

Example text

As a general rule of thumb, percentile values are unlikely to be affected to any significant extent until the minority group reach 30% of the total or more. g. guarding of machinery; see Thompson and Booth 1982). 3). In the chapters that follow we shall treat this as the standard reference population on which we shall base our design recommendations and other anthropometric calculations. , will be found in Chapter 10. e. the crown of the head). Applications As a cross-referencing dimension for comparing populations and estimating data; defines the vertical clearance required in the standing workspace; minimal acceptable height of overhead obstructions such as lintels, roofbeams, light fittings, etc.

Combining these data with the above criterion gives us the upper and lower limits of optimal working level. EH– 50=1065 [52]; EH–100=1015 [52]. We can treat these just as if they are new normally distributed anthropometric dimensions—and calculate the percentile in these distributions to which any particular workbench height corresponds. 6 Criteria for optimal and satisfactory working heights in an industrial assembly task. 2 Calculation of percentage of men accommodated by a workbench that is 1000 mm in height should bear in mind that the criteria refer to ‘optimal’ bench heights.

In theory, the resultant ‘unisex’ distribution is platykurtic. In practice, the deviations from normality are so small that we can ignore them. The only alternative, which avoids the assumption of normality, is to determine percentiles directly by simply counting heads—but since this requires large numbers of subjects it is rarely feasible and few datasets in the literature have been established with this degree of certainty. In general, the best practice is to assume normality but to proceed with circumspection in those situations (mentioned above) where we have reason to doubt the assumption.

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