TY - GEN
T1 - Eye Tracking Analytics for Mental States Assessment - A Review
AU - Li, Fan
AU - Xu, Gangyan
AU - Feng, Shanshan
N1 - Funding Information:
* Research is supported by the National Research Foundation, Singapore.
Publisher Copyright:
© 2021 IEEE.
PY - 2022/1/6
Y1 - 2022/1/6
N2 - Objectively measuring and monitoring human mental states in a non-intrusive way is important in improving the context-awareness of smart objects. One of the suitable bio-signals in measuring human mental states is aye-tracking data, as visual is the first channel of information collection. In addition, eye-tracking data shows the process of human-system interactions. Traditionally, many studies have been conducted to investigate the correlations between eye-tracking data and human mental states. Recently, with advanced artificial intelligence algorithms, the spatial and temporal patterns of eye-tracking data can be deeply analyzed for detecting human mental states. This study aims to explore and review eye-tracking parameters and state-of-art methods for mental states assessments. The study reveals that both statistical methods and novel methods, such as machine learning and deep learning have been applied to process eye-tracking data. Besides, novel features extracted from eye-tracking data, such as gaze-bin and entropy have been used in assessing human mental states. This review is expected to provide references for eye-tracking data analysis.
AB - Objectively measuring and monitoring human mental states in a non-intrusive way is important in improving the context-awareness of smart objects. One of the suitable bio-signals in measuring human mental states is aye-tracking data, as visual is the first channel of information collection. In addition, eye-tracking data shows the process of human-system interactions. Traditionally, many studies have been conducted to investigate the correlations between eye-tracking data and human mental states. Recently, with advanced artificial intelligence algorithms, the spatial and temporal patterns of eye-tracking data can be deeply analyzed for detecting human mental states. This study aims to explore and review eye-tracking parameters and state-of-art methods for mental states assessments. The study reveals that both statistical methods and novel methods, such as machine learning and deep learning have been applied to process eye-tracking data. Besides, novel features extracted from eye-tracking data, such as gaze-bin and entropy have been used in assessing human mental states. This review is expected to provide references for eye-tracking data analysis.
UR - http://www.scopus.com/inward/record.url?scp=85124314065&partnerID=8YFLogxK
U2 - 10.1109/SMC52423.2021.9658674
DO - 10.1109/SMC52423.2021.9658674
M3 - Conference article published in proceeding or book
AN - SCOPUS:85124314065
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 2266
EP - 2271
BT - 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
Y2 - 17 October 2021 through 20 October 2021
ER -