Preliminary validation of a tool for visualizing anthropometric data

Accurate representations of anthropometry are necessary for effective digital human models. It has been known for decades that inappropriate application of univariate anthropometric data to multivariate problems yields suboptimal design performance. However, traditional design tools including textbooks and univariate data tables do not foster an understanding about appropriate means for solving multivariate problems. On the other hand, quantitative design tools that permit visualization of large anthropometry datasets are better suited for exploring more complicated problems with multiple dimensions. One such tool—a dynamic web interface—is investigated in the present research and its performance is compared with an existing technique—univariate tables—for incorporating anthropometric data into design. This paper presents preliminary research for developing intuitive tools that enable designers to properly solve multivariate problems. A web-based pilot study involving 40 participants from Penn State University and the Army Research Lab at Aberdeen Proving Ground was devised to evaluate an interactive web interface for working with anthropometric data. A within-subjects study was designed to assess each the ability of participants (i.e., designers) to correctly identify the range in anthropometry necessary to achieve a specified accommodation level in a timely fashion. Self-rated subjective measures of performance were also obtained. The performance of the participant using the web tool was compared with their performance using a similar tabular tool, wherein percentiles of the requested anthropometric dimensions were supplied. For both tools, the US Army's ANSUR data were used, but any large anthropometric database composed of a variety of measures could be substituted without affecting the methodology or findings. The results of the pilot study show that responses exhibited better accuracy and estimates of population accommodation using the web tool compared with the tables. Subjective measures of performance indicate that participants judged the results of the web tool to be more likely to influence non-expert decision makers, better aid their understanding of the characteristics of accommodated data, and provide more compelling evidence for decision-making, compared with the tables.