Considering Human Variability When Implementing Product Platforms

Garneau, Christopher J.; Nadadur, Gopal; Parkinson, Matthew B.

doi:10.1007/978-1-4614-7937-6_22

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…Design for human variability is a Design for X approach to user-centered design that focuses on quantifiable, physical characteristics of the user [4]. Utilizing existing anthropometric databases, such as ANSUR [37], users can be virtually fit to a product to assess how well they are spatially accommodated [40]. Statistical modeling can be performed to infer missing data, model correlation between human parameters, and extend the usefulness of the data in practice [41][42][43].…”

Section: Modeling Variability With Existing Human Datamentioning

confidence: 99%

Virtual Modeling of User Populations and Formative Design Parameters

Knisely

Vaughn-Cooke

2020

Systems

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Human variability related to physical, cognitive, socio-demographic, and other factors can contribute to large differences in human performance. Quantifying population heterogeneity can be useful for designers wishing to evaluate design parameters such that a system design is robust to this variability. Comprehensively integrating human variability in the design process poses many challenges, such as limited access to a statistically representative population and limited data collection resources. This paper discusses two virtual population modeling approaches intended to be performed prior to in-person design validation studies to minimize these challenges by: (1) targeting recruitment of representative population strata and (2) reducing the candidate design parameters being validated in the target population. The first approach suggests the use of digital human models, virtual representations of humans that can simulate system interaction to eliminate candidate design parameters. The second approach suggests the use of existing human databases to identify relevant human characteristics for representative recruitment strata in subsequent studies. Two case studies are presented to demonstrate each approach, and the benefits and limitations of each are discussed. This paper demonstrates the benefit of modeling prior to conducting in-person human performance studies to minimize resource burden, which has significant implications on early design stages.

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Section: Modeling Variability With Existing Human Datamentioning

confidence: 99%

Virtual Modeling of User Populations and Formative Design Parameters

Knisely

Vaughn-Cooke

2020

Systems

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“…Design for human variability (DfHV) attempts to address the subjective nature of user-centered design by focusing on quantifying and integrating human physical attributes in to the design process. Garneau, Nadadur, and Parkinson [9] define DfHV as "the design of artifacts, tasks, and environments that are robust to the variability in their users." Human variability is described as the anthropometric differences in human beings [10], based on the measurement of the human body [11].…”

Section: Human Variabilitymentioning

confidence: 99%

Design and Validation of a Method to Characterize Human Interaction Variability

Cage¹,

Vaughn-Cooke²,

Fuge³

2020

Systems

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Human interactions are paramount to the user experience, satisfaction, and risk of user errors. For products, anthropometry has traditionally been used in product sizing. However, structured methods that accurately map static and dynamic capabilities (e.g., functional mapping) of musculoskeletal regions for the conceptualization and redesign of product applications and use cases are limited. The present work aims to introduce and validate the effectiveness of the Interaction Variability method, which maps product components and musculoskeletal regions to determine explicit design parameters through limiting designer variation in the classification of human interaction factors. This study enrolled 16 engineering students to evaluate two series of interactions for (1) water bottle and (2) sunglasses applications enabling method validity and designer consistency assessments. For each interaction series, subjects identified and characterized product applications, components, and human interaction factors. Primary interactions, product mapping, and application identification achieved consensus between ranges of 31.25% and 100.00%, with significance (p < 0.1) observed at consensus rates of ≥75.00%. Significant levels of consistency were observed amongst designers, for at least one measure in all phases except anthropometric mapping for the sunglasses application indicating method effectiveness. Interaction variability was introduced and validated in this work as a standardized approach to identify, define, and map human and product interactions, which may reduce unintended use cases and user errors, respectively, in consumer populations.

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Sustainable product development: the intersection of Design for X, Big Data and Industrial Internet of Things with fuzzy logic theory

Benabdellah

Zekhnini

2021

2021 Third International Conference on Transportation and Smart Technologies (TST)

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Considering Human Variability When Implementing Product Platforms

Cited by 4 publications

References 39 publications

Virtual Modeling of User Populations and Formative Design Parameters

Virtual Modeling of User Populations and Formative Design Parameters

Design and Validation of a Method to Characterize Human Interaction Variability

Sustainable product development: the intersection of Design for X, Big Data and Industrial Internet of Things with fuzzy logic theory

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