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Issue title: Computer Ergonomics: The State of the Art
Guest editors: Thomas J. Albin
Article type: Research Article
Authors: Huang, Yanquna; * | Li, Xub | Zhang, Jiec
Affiliations: [a] Tianjin Key Laboratory of Equipment Design and Manufacturing Technology, Tianjin University, Tianjin, China | [b] School of Mechanical and Electronic Engineering, Tianjin Polytechnic University, Tianjin, China | [c] Tianjin Sino-German Vocational Technical College, Tianjin, China
Correspondence: [*] Address for correspondence: Yanqun Huang, School of Mechanical Engineering, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin 300072, P. R. China. Tel.: +86 13821893956; Fax: +86 02227406260; E-mail: yanq.huang@gmail.com
Abstract: BACKGROUND:The discrepancy between human cognition and machine requirements/behaviors usually results in serious mental thinking mapping loads or even disasters in product operating. It is important to help people avoid human-machine interaction confusions and difficulties in today’s mental work mastered society. OBJECTIVE:Improving the usability of a product and minimizing user’s thinking mapping and interpreting load in human-machine interactions. METHODS:An optimal human-machine interface design method is introduced, which is based on the purpose of minimizing the mental load in thinking mapping process between users’ intentions and affordance of product interface states. By analyzing the users’ thinking mapping problem, an operating action model is constructed. According to human natural instincts and acquired knowledge, an expected ideal design with minimized thinking loads is uniquely determined at first. Then, creative alternatives, in terms of the way human obtains operational information, are provided as digital interface states datasets. In the last, using the cluster analysis method, an optimum solution is picked out from alternatives, by calculating the distances between two datasets. RESULTS:Considering multiple factors to minimize users’ thinking mapping loads, a solution nearest to the ideal value is found in the human-car interaction design case. CONCLUSIONS:The clustering results show its effectiveness in finding an optimum solution to the mental load minimizing problems in human-machine interaction design.
Keywords: Usability, mental load, user model, interface design
DOI: 10.3233/WOR-152112
Journal: Work, vol. 52, no. 2, pp. 433-440, 2015
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