Mobile Phones and Blood Glucose Meters are widely used personal digital input/output devices of great aesthetic and functional importance to their users. Interactive Genetic Algorithms (IGAs) are used to test for aesthetic preference links between these two devices. IGAs mimic natural selection by repeatedly having a human tester select designs they find the most aesthetically pleasing to serve as the parents of the next generation. The web-based IGA was used by 22 participants; varying button spacing, screen size, and radius of phones and glucose meters over 8 trials. Several links were found between the devices, specifically the screen-area to keypad-area ratio, and screen height to screen width ratio. IGAs show promise as a tool for designers to use a similar product to reduce the risk in a new product’s design by building on user’s current aesthetic experience.
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References
1.
BauerlyM.LiuY. (2009). Evaluation and Improvement of Interface Aesthetics with an Interactive Genetic Algorithm (IGA). International Journal of Human Computer Interaction, 25(2), 155-166.
2.
Centers for Disease Control and Prevention. (2011). National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: U.S. Department of Health and Human Services, CDC.
3.
ConsN.JennyJ. (1994, oct). Comparing perceptions of dental aesthetics in the USA with those in eleven ethnic groups. Int Dent J., 44(6), 489-94.
4.
GongJ.TarasewichP. (2004). Guidelines for handheld mobile device interface design. 2004 DSI Annual Meeting (pp. 3751-3756). In Proceedings of the 2004 DSI Annual Meeting.
5.
HanS. H.KimK. J.YunM. H.HongS. W.KimJ. (2004). Identifying Mobile Phone Design Features Critical to User Satisfaction. Human Factors and Ergonomics in Manufacturing, 14, 15-29.
6.
HughesM. D. (2009, September). The Business of Self-Monitoring of Blood Glucose: A Market Profile. J Diabetes Sci Technol, 3(5), 1219-1223.
7.
ITU. (2010). Measuring the Information Society. International Telecommunication Union. Geneva, Switzerland: ITU.
8.
KarlssonP.DjabriF. (2001). Analogue Styled User Interfaces: An Exemplified Set of Principles Intended to Improve Aesthetic Qualities in Use. Mobile HCI 2001: Third International Workshop on Human-Computer Interaction with Mobile Devices.
9.
KellyJ. C.PapalambrosP.WakefieldG. (2006). The Development of a Tool for the Preference Assessment of the Visual Aesthetics of an Object Using Interactive Genetic Algorithms. 9th Generative Art Conference. Milan, Italy.
10.
LiuY. (2003). Engineering aesthetics and aesthetic ergonomics: Theoretical foundations and a dual-process research methodology. Ergonomics, 46, 1273-1292.
11.
LloydL. (2010, April). Survival of the Smartest. MD+DI;Medical Device and Diagnostic Industry, 32(4).
12.
Nathan-RobertsD. (in-press). Using Interactive Genetic Algorithms to Support Aesthetic Ergonomic Design. Dissertation, University of Michigan. Ann Arbor, Michigan: ProQuest/UMI.
13.
Nathan-RobertsD.LiuY. (2010). Investigation of Relative Mobile Phone Size Preference Using Interactive Genetic Algorithms. 54th Annual Meeting of the Human Factors and Ergonomics Society. San Francisco, California: HFES.
14.
Nathan-RobertsD.KellyJ. C.LiuY. (2011). Determining the Effect of Users‘ Mobile Phone on Design Preference via Interactive Genetic Algorithms. 55th Annual Meeting of the Human Factors and Ergonomics Society. Las Vegas, Nevada: HFES.
15.
Nathan-RobertsD.LiuY.YoungM.HaysJ. D.An SoC.LittleC.ZhaoL. H. (2009). Content Analysis of Literature on Mobile Phone Design Features. 17th World Congress on Ergonomics. Beijing, China: IEA.
16.
NobletJ. (1993). Industrial Design: Reflections of a Century. Paris: Abbeville Press, Inc.
17.
OttoK. N.WoodK. L. (2001). Product Design: Techniques in Reverse Engineering and New Product Development. Prentice Hall.
18.
PlosO.BuisineS. (2006). Universal design for mobile phones: a case study. Proceedings of the ACM Conference on Human Factors in Computing Systems (pp. 1229-34). Montréal, Québec, Canada: ACM.
19.
Pomales-GarciaC.LiuY.MendezD. (2005). Web-Based Distance Learning Technology: Does Appearance Matter. Human Factors and Ergonomics Society Annual Meeting Proceedings, 49, 755–758.
20.
RogersW. A.MykityshynA. L.CampbellR. H.FiskA. D. (2001, Winter). Analysis of a Simple Medical Device. Ergonomics in Design, 9(1), pp. 6-14.
21.
SauerJ.SondereggerA. (2009). The influence of prototype fidelity and aesthetics of design in usability tests: Effects on user behaviour, subjective evaluation and emotion. Applied Ergonomics, 670-677.
22.
SmythS. N.WallaceD. R. (2000). Towards the Synthesis of Aesthetic Product Form.
23.
SondereggerA.SauerJ. (2010, may). The influence of design aesthetics in usability testing: Effects on user performance and perceived usability. Applied Ergonomics, 49, 403–410.
24.
StoryM. F.LuceA. C.RempelD. M. (2009, October). Accessibility and Usability of Diabetes Management Devices for Users with Vision Disabilities. Human Factors and Ergonomics Society Annual Meeting Proceedings, 53(11), 699-703(5).
25.
SzajnaB. (1996). Empirical Evaluation of the Revised Technology Acceptance Model. Management Science, 42, pp. 85-92.
26.
VeryzerR. W. (1998). Key Factors Affecting Customer Evaluation of Discontinuous New Products. Journal of Product Innovation Management, 15(2), 136-150.
