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Anthropometric Procedures for Protective Equipment Sizing and Design
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2 2013
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Source: Hum Factors. 55(1):6-35
Details:
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Alternative Title:Hum Factors
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Personal Author:
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Description:Objectives
This article presented four anthropometric theories (univariate, bivariate/probability distribution, multivariate, and shape-based methods) for protective equipment design decisions.
Background
While the significance of anthropometric information for product design is well recognized, designers continue to face challenges in selecting efficient anthropometric data processing methods and translating the acquired information into effective product designs.
Methods
For this study, 100 farm tractor operators, 3,718 respirator users, 951 firefighters, and 816 civilian workers participated in four studies on the design of tractor roll-over protective structures (ROPS), respirator test panels, fire truck cabs, and fall-arrest harnesses, respectively. Their anthropometry and participant-equipment interfaces were evaluated.
Results
Study 1 showed a need to extend the 90-cm vertical clearance for tractor ROPS in the current industrial standards to 98.3 to 101.3 cm. Study 2 indicated that current respirator test panel would have excluded 10% of the male firefighter population; a systematic adjustment to the boundaries of test panel cells was suggested. Study 3 provided 24 principal component analysis-based firefighter body models to facilitate fire truck cab design. Study 4 developed an improved gender-based fall-arrest harness sizing scheme to supplant the current unisex system.
Conclusions
This article presented four anthropometric approaches and a six-step design paradigm for ROPS, respirator test panel, fire truck cab, and fall-arrest harness applications, which demonstrated anthropometric theories and practices for defining protective equipment fit and sizing schemes.
Applications
The study provided a basis for equipment designers, standards writers, and industry manufacturers to advance anthropometric applications for product design and improve product efficacy.
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Pubmed ID:23516791
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Pubmed Central ID:PMC4562332
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Volume:55
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Issue:1
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Supporting Files
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