A comparison of methodologies for designing for human variability

Garneau, Christopher J.; Parkinson, Matthew B.

doi:10.1080/09544820903535404

Cited by 26 publications

(14 citation statements)

References 30 publications

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“…The stochastic variables were added to ensure that the variability for each workstation setting can be explained by both anthropometric measurements and user preferences. For further details, refer to Garneau & Parkinson (2011). The four regression models (1) – (4) have an R 2 value of 0.47, 0.8, 0.44, and 0.66, respectively (all p < .001).…”

Section: Resultsmentioning

confidence: 99%

“…Then, a virtual population of PH and TH were estimated by using the ST of males and females (5647 and 5971, respectively) from the NHANES 2007–2010 data (weights carried out appropriately) into the above proportionality equations (Garneau & Parkinson, 2011). …”

Section: Resultsmentioning

confidence: 99%

“…In the design of artifacts interacting with human users, the understanding of body dimensions and capabilities of the target population is critical in terms of maximizing fit, safety, and performance (HFES 300 Committee, 2004). Body measures have been used in various product design processes such as workstation designs, bicycle seat designs, and aircraft designs (Das & Sengupta, 1996; Garneau & Parkinson, 2011; Joslin, 2014). The concept of incorporating anthropometric measures should not be an exception for using exercise equipment in the office workplace.…”

Section: Introductionmentioning

confidence: 99%

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Utilizing anthropometric data to improve the usability of desk bikes, and influence of desk bikes on reading and typing performance

2017

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This study investigated the feasibility of using a desk bike in an office setting. Workstation measurements were introduced to accommodate 95% of the general U.S. population in using desk bikes. Reading and typing performances were compared at three different cycling conditions (no cycling, 10 and 25 watts). Thirty healthy individuals (15 female and 15 male; Age mean: 23.1, σ: 4.19) were recruited based on 5/50/95th percentile stature. Participants were required to select preferred workstation settings and perform reading and typing tasks while pedaling. According to anthropometric measurements and variability from user preference, recommended adjustable ranges of workstation settings for the general U.S. population were derived. Repeated measures ANOVA showed that pedaling had no significant effect on reading comprehension (p > .05), but had significant effect on typing performance (p < .001). A preferred level of cycling intensity was determined (mean 17.3 watts, σ: 3.69).

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Utilizing anthropometric data to improve the usability of desk bikes, and influence of desk bikes on reading and typing performance

2017

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“…saddle height and handle bar position in bicycle design (19,56). Enriched SSMs allows analysis of intra-cluster variations in advance, such that designers can anticipate e.g.…”

Section: Potential For Fit Within Clustersmentioning

confidence: 99%

“…Their statistical distribution is univariate, by definition. It is straightforward to link such data on human sizes with the dimensions of a product, or vice versa (18,19) (20).…”

Section: Linking Body Sizes To Product Sizesmentioning

confidence: 99%

A new data structure and workflow for using 3D anthropometry in the design of wearable products

Verwulgen

Lacko

Vleugels

et al. 2018

International Journal of Industrial Ergonomics

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Abstract.The human body is a complex biomechanical system that exhibits many variations. Wearable products should be both functional and comfortable. They require a close and accurate fit to the body of the end-user. Current approaches to design body near products rely on 1D anthropometry and unrealistic manikins, e.g. constructed from simple surfaces such as spheres and cylinders connected by splines. With the uprising of 3D scanning, a myriad of accurate 3D body models becomes available. In this paper we present a framework to use this 3D shape information in the development of wearable products. The key concept that we introduce to achieve this extension, is an enriched shape model: a statistical shape model of the human body that also contains all 1D anthropometric data in it. With enriched shape models, a 3D shape can be parameterized with a given set of anthropometric features. Thus the dense geometric information of an individual's shape can be obtained simply by tuning that individual's anthropometric values. By designing on the generated 3D surface, a product can be obtained that closely fits the individual's shape. We thus extend the method of linking 1D anthropometric data with the dimensions of a product. This results in three design strategies that link both body shape with product geometry: design for collective fit, design for fit within clusters and design for individual fit. Each strategy is explained and studied with the design of wearable EEG headsets that fits the human head. KeywordsMass-customization, EEG headsets, CAD, parameterized design, 3D anthropometry, statistical shape models Relevance to Design PracticeWe present a workflow to use accurate 3D shape models of the human body in the design of products that should closely fit the end-user. To that end, we introduce enriched shape models: a new data structure that contains all dense geometric shape information together with classical anthropometric data. We illustrate how enriched shape models can be used to achieve products with personalized fit, as an extension to the use of univariate anthropometric data. The use of enriched shape models for personalized design could become an important driver for mass customization. To that end, tools and techniques should be developed to incorporate the presented workflow in CAD/CAM.

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