Eye Tracking, Usability, and User Experience: A Systematic Review

被引:35
作者
Novak, Jakub Stepan [1 ]
Masner, Jan [1 ]
Benda, Petr [1 ]
Simek, Pavel [1 ]
Merunka, Vojtech [2 ]
机构
[1] Czech Univ Life Sci Prague, Dept Informat Technol, Prague, Czech Republic
[2] Czech Univ Life Sci Prague, Dept Informat Engn, Prague, Czech Republic
关键词
Eye tracking; UX; machine learning; usability; EMOTION RECOGNITION; COMPLEXITY; MOVEMENTS; DECISIONS; MOUSE; MODEL; GAZE;
D O I
10.1080/10447318.2023.2221600
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Usability and user experience (UX) are emerging concerns around not only application development but everything designed to be used by people. Evaluation of the UX is, by nature, intensely subjective and time-consuming. The article focuses mainly on Eye Tracking, Usability, and User Experience from a general point of view, with an emphasis on automatic data processing. In recent years, new technological approaches have been emerging to quantify usability testing data and improve process automation. Eye tracking technology is a great way to analyze users' interaction with the product, allowing researchers to discover usability issues and even leverage the power of machine learning to recognize various kinds of emotions linked to users' interactions. Existing research concerned with these three main topics has been methodically explored. For this review, we extensively searched 1988 theme-related articles. One hundred and forty-four articles were selected based on meticulous screening, from which 90 were included in this systematic review. The outcomes reveal a significant shift toward a more technologically advanced evaluation of user experience and usability in various areas. The review proposes several opportunities for future research and missing areas connecting user experience, eye tracking, and machine learning into more products focused on problem pattern identification.
引用
收藏
页码:4484 / 4500
页数:17
相关论文
共 105 条
[41]  
Jhani Adre de Bruin B., 2014, AUTOMATED USABILITY
[42]   On the Improvement of Eye Tracking-Based Cognitive Workload Estimation Using Aggregation Functions [J].
Kaczorowska, Monika ;
Karczmarek, Pawel ;
Plechawska-Wojcik, Malgorzata ;
Tokovarov, Mikhail .
SENSORS, 2021, 21 (13)
[43]  
Katona J, 2021, ACTA POLYTECH HUNG, V18, P193
[44]  
Katona J, 2017, INT CONF COGN INFO, P407, DOI 10.1109/CogInfoCom.2017.8268280
[45]   Classifying Mobile Eye Tracking Data With Hidden Markov Models [J].
Kit, Dmitry ;
Sullivan, Brian .
PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON HUMAN-COMPUTER INTERACTION WITH MOBILE DEVICES AND SERVICES (MOBILEHCI 2016), 2016, :1037-1040
[46]   Predicting Final User Satisfaction Using Momentary UX Data and Machine Learning Techniques [J].
Koonsanit, Kitti ;
Nishiuchi, Nobuyuki .
JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH, 2021, 16 (07) :3136-3156
[47]  
Krol M, 2017, JUDGM DECIS MAK, V12, P596
[48]   Effects of Individuality, Education, and Image on Visual Attention: Analyzing Eye-tracking Data using Machine Learning [J].
Lee, Sangwon ;
Hwang, Yongha ;
Jin, Yan ;
Ahn, Sihyeong ;
Park, Jaewan .
JOURNAL OF EYE MOVEMENT RESEARCH, 2019, 12 (02)
[49]   Eye Gaze and Interaction Differences of Holistic Versus Analytic Users in Image-Recognition Human Interaction Proof Schemes [J].
Leonidou, Pantelitsa ;
Constantinides, Argyris ;
Belk, Marios ;
Fidas, Christos ;
Pitsillides, Andreas .
HCI FOR CYBERSECURITY, PRIVACY AND TRUST (HCI-CPT 2021), 2021, 12788 :66-75
[50]   Evaluating the Performance of Machine Learning Algorithms in Gaze Gesture Recognition Systems [J].
Li, Jiayao ;
Ray, Samantha ;
Rajanna, Vijay ;
Hammond, Tracy .
IEEE ACCESS, 2022, 10 :1020-1035