A biometric usability evaluation of instrument cluster and infotainment systems in two hybrid cars

Automobile instrument clusters and infotainment systems are commonly used and prominent in modern cars and trucks. Thus, the design of these systems and their usability can significantly affect driving performance and satisfaction. This study demonstrates how biometric sensor methods can be used to assess such usability. Specifically, the study examines users' visual attention and affective states in response to instrumentation in two hybrid cars. The study combines eye tracking, facial expression analysis, and electroencephalography (EEG) data to investigate how these two cluster and infotainment designs (a) capture visual attention; (b) potentially inspire engagement, frustration and confusion; and (c) impact users' perception of ease of use.

[1]  Winslow Burleson,et al.  Affect Measurement: A Roadmap Through Approaches, Technologies, and Data Analysis , 2017 .

[2]  R. Krauss,et al.  Facial and autonomic manifestations of the dimensional structure of emotion , 1984 .

[3]  Charles Spence,et al.  The Multisensory Driver: Implications for Ergonomic Car Interface Design , 2012 .

[4]  Joseph H. Goldberg,et al.  Identifying fixations and saccades in eye-tracking protocols , 2000, ETRA.

[5]  Alan Kennedy,et al.  Book Review: Eye Tracking: A Comprehensive Guide to Methods and Measures , 2016, Quarterly journal of experimental psychology.

[6]  J. E. Rose,et al.  Autonomic Nervous System Activity Distinguishes Among Emotions , 2009 .

[7]  R. Levenson Autonomic Nervous System Differences among Emotions , 1992 .

[8]  Albrecht Schmidt,et al.  Automotive user interfaces: human computer interaction in the car , 2010, CHI Extended Abstracts.

[9]  Dongsong Zhang,et al.  Challenges, Methodologies, and Issues in the Usability Testing of Mobile Applications , 2005, Int. J. Hum. Comput. Interact..

[10]  Zhiwei Zhu,et al.  Real-time nonintrusive monitoring and prediction of driver fatigue , 2004, IEEE Transactions on Vehicular Technology.

[11]  J. Cohn,et al.  Automated Face Analysis for Affective Computing , 2015 .

[12]  Kelly Caine,et al.  Understanding Your Users: A Practical Guide to User Research Methods , 2015 .

[13]  Jakob Nielsen,et al.  Heuristic evaluation of user interfaces , 1990, CHI '90.

[14]  Hema Pavithra.S,et al.  Real-Time Nonintrusive Monitoring And Prediction Of Driver Fatigue , 2016 .

[15]  Laura Chamberlain Eye Tracking Methodology; Theory and Practice , 2007 .

[16]  Mike Kuniavsky,et al.  Smart Things: Ubiquitous Computing User Experience Design , 2010 .

[17]  Michelle N. Lumicao,et al.  EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. , 2007, Aviation, space, and environmental medicine.