Gopal Nadadur

The role of anthropometry in designing for sustainability

An understanding of human factors and ergonomics facilitates the design of artefacts, tasks and environments that fulfil their users’ physical and cognitive requirements. Research in these fields furthers the goal of efficiently accommodating the desired percentage of user populations through enhanced awareness and modelling of human variability. Design for sustainability (DfS) allows for these concepts to be leveraged in the broader context of designing to minimise negative impacts on the environment. This paper focuses on anthropometry and proposes three ways in which its consideration is relevant to DfS: reducing raw material consumption, increasing usage lifetimes and ethical human resource considerations. This is demonstrated through the application of anthropometry synthesis, virtual fitting, and sizing and adjustability allocation methods in the design of an industrial workstation seat for use in five distinct global populations. This work highlights the importance of and opportunities for using ergonomic design principles in DfS efforts. Practitioner Summary: This research demonstrates the relevance of some anthropometry-based ergonomics concepts to the field of design for sustainability. A global design case study leverages human variability considerations in furthering three sustainable design goals: reducing raw material consumption, increasing usage lifetimes and incorporating ethical human resource considerations in design.

Using Designing for Human Variability to optimize Aircraft eat Layout

Integrating the seemingly divergent objectives of aircraft seat configuration is a difficult task. Aircraft manufacturers look to design seats to maximize customer satisfaction and in-flight safety, but these objectives can conflict with the profit motive of airline companies. In order to boost revenue by increasing the number of passengers per aircraft, airline companies may increase seat height and decrease seat pitch in order to increase the number of passengers on the plane. This results in disaccommodation of a greater percentage of the passenger population and is a reason for rising customer dissatisfaction. This paper describes an effort to bridge this gap by incorporating digital human models, layout optimization, and a profit-maximizing constraint into the aircraft seat design problem. A simplified aircraft seat design experiment is conceptualized and its hypothetical results are extrapolated to an airline passenger population. The dependence of passengers’ comfort ratings on seat height and seat pitch are analyzed, with the aim being to study how to maximize the design’s profitability while not compromising on comfort. This is a specific example of a general problem: modeling the design space of multi-objective problems involving designing for human variability. These multiple and often conflicting objectives can exist purely within the design (e.g., minimizing weight while maximizing strength) or can involve higher-level targets such as satisfying customers, passing regulatory requirements, and achieving profits (unfortunately these are not always congruent).

Consideration of Demographics and Variance in Regression Approaches to Estimating Body Dimensions for Spatial Analysis of Design

A common objective in designing for human variability is to consider the variability in body size and shape of the target user population. Since anthropometric data specific to the user population of interest are seldom available, the variability is approximated. This is done in a number of ways, including the use of data from populations that are well-documented (e.g., the military), proportionality constants, and digital human models. These approaches have specific limitations, including a failure to consider the effects of lifestyle and demography resulting in products, tasks, and environments that are inappropriately sized for the actual user population, causing problems with safety, fit, and performance. This paper explores a regression-based approach in a context where the demographic distributions of descriptors (e.g., race/ethnicity, age, and fitness) are dissimilar for the database and target population. Also examined is a stratified regression model involving the development of independent anthropometry-estimation models for each racial group. When using regression with residual variance, stratification on the predictor demographics to obtain estimates of gender, stature, and BMI distributions is shown to be sufficiently robust for usual database-target population combinations. Consideration of demographic variables in development of the regression model provides marginal improvement, but could be appropriate in specific situations.

Strategic Product Design for Multiple Global Markets

Designing products for multiple global user populations has been the focus of numerous studies. Based on an understanding of the current state of knowledge, this paper outlines three broad top-down design strategies to satisfy varying user, business, and regulatory requirements across target markets, and to do so in a resource-efficient manner. The first strategy results in a set of optimal, robust, or reconfigurable designs for the markets. The second strategy is the development of a static platform-based design, with variants of the product for the different target markets. In the third strategy, flexibility is embedded into a platform itself; the manufacturer can then release the product in a few target markets, and can leverage the platform at later stages when entering additional markets or adapting to changing requirements. The implementation of the proposed strategies is explored in the context of three different products: (1) the Adidas Jabulani soccer ball, (2) left- and right- hand drive vehicles, and (3) the Apple suite of mobile and tablet devices. The observations in these case studies highlight the importance of the three global product design strategies, and help define certain questions for future research.Copyright

Anthropometry for a North American Manufacturing population.

Digital Human Models are used extensively in virtual manufacturing to evaluate hand clearance and reach. Spatial assessments of accommodation are typically conducted using digital human models representative of the manufacturing population. Unfortunately, these models are often based on anthropometry gathered from sources that are not representative of the actual target worker population. For example, the size and shape might be based on data from the U.S. military, which differs in fitness, age, and race distributions from the typical automotive manufacturing population. Ford ergonomists traced errors in accommodation predictions to these inaccurate representations. Using a recently developed statistical methodology incorporating principal components analysis, the anthropometry of the target worker population was synthesized. Using these new data, Ford updated the anthropometry of their digital human models to reflect changes due to secular trends in the U.S. The models also consider the diversity in age and race, thus producing Ford manikins that better represent the variability in their worker base. The improved ability to accurately predict accommodation allows for significant immediate and long-term reductions in engineering costs.

Considering Secular and Demographic Trends in Designing for Present and Future Populations

In products designed for human variability, the anthropometry (body measurements) of the target user population constitutes a primary source of variability that must be considered in the optimization of the spatial dimensions of the product. Accommodation, which describes the ability of a user to interact with a device or environment in their preferred manner, is a key measure of its performance. Other studies have considered various methods for accounting for the variability in anthropometry in a target user population to calculate estimated accommodation, but few have explicitly considered the effects of secular trends and demographic changes over time. This paper considers these changes in the context of a case study involving truck drivers and cab geometry. The truck driver populations are used to illustrate changes in body size and shape over a 30-year period and show how they affect user acceptability of designs. Changes in the gender split of the driver population are also considered, and are shown to have a significant effect on accommodation. The work demonstrates that secular trends and demographic changes over time significantly affect accommodation, but a well designed product will be more robust to these changes.Copyright

Application of the Generational Variety Index: A Retrospective Study of iPhone Evolution

The generational variety index (GVI) helps to identify the components of product variants that are most likely to require redesign in the future. These components can then be embedded with the flexibility required for them to be easily modifiable; the remaining components can be designed into a platform. This paper describes the application of the GVI technique in studying the evolution of the Apple iPhone, which was first released in 2007 and has since undergone multiple redesigns. The analysis includes the five generations of the iPhone (original, 3G, 3GS, 4, and 4S) and focuses primarily on mechanical subsystems. The results of the analysis and subsequent design recommendations are compared with the actual design evolution of the iPhone product line. For certain subsystems, this comparison reveals a divergence in Apple’s design decision-making from the evolution recommended by the GVI technique. Limitations include its retrospective nature and the use of only publicly available data.

A Z-Score-Based Method to Synthesize Anthropometric Datasets for Global User Populations

Globalized marketplaces are necessitating the consideration of the needs of users from a variety of national and international regions. Relevant body dimensions are known to play a key role in influencing users’ physical interactions with products. The main challenge in designing these products is the unavailability of comprehensive anthropometric databases for detailed analyses and decision-making. This paper presents a new method to this end. Z-scores are computed for each body measure of every individual in a reference population; this can be any population for which a comprehensive database is available. Next, descriptive statistical information (e.g., means, standard deviations, by-percentile values) from numerous studies and surveys are used to estimate distributions of the required body dimensions. Finally, the z-score values from the reference population are utilized to sample from the aforementioned distributions in order to synthesize the requisite virtual target population of users. The z-score method is demonstrated in the context of two existing populations: U.S. military in the late 1980s (ANSUR) and Japanese youth from the early 1990s. Despite certain stated limitations, which are topics of future work in this line of research, the method is shown to be accurate, easy-to-apply, and robust in terms of underlying assumptions.© 2013 ASME

Considering Human Variability When Implementing Product Platforms

Design for Human Variability (DfHV) is the practice of designing artifacts, tasks, and environments that are robust to the variability in their users. Designs often incorporate adjustability and/or offer several sizes to account for the different requirements of the target user population. There are several situations where DfHV can provide platforming opportunities that might otherwise be overlooked. This chapter provides a brief introduction to DfHV, outlines some basic techniques, and provides a description of scenarios where platforming and modularity might be a good approach.

Designing for multiple global user populations: increasing resource allocation efficiency for greater sustainability.

This paper proposes a method to identify opportunities for increasing the efficiency of raw material allocation decisions for products that are simultaneously targeted at multiple user populations around the world. The values of 24 body measures at certain key percentiles were used to estimate the best-fitting anthropometric distributions for female and male adults in nine national populations, which were selected to represent the diverse target markets multinational companies must design for. These distributions were then used to synthesize body measure data for combined populations with a 1:1 female:male ratio. An anthropometric range metric (ARM) was proposed for assessing the variation of these body measures across the populations. At any percentile, ARM values were calculated as the percentage difference between the highest and lowest anthropometric values across the considered user populations. Based on their magnitudes, plots of ARM values computed between the 1st and 99 th percentiles for each body measure were grouped into low, medium, and high categories. This classification of body measures was proposed as a means of selecting the most suitable strategies for designing raw material-efficient products. The findings in this study and the contributions of subsequent work along these lines are expected to help achieve greater efficiencies in resource allocation in global product development.