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Real-Time Assessment of the Cross-Task Mental Workload Using Physiological Measures During Anomaly Detection
Zhao, Guozhen1,2; Liu, Yong-Jin3; Shi, Yuanchun3; Yong-Jin Liu
2018-04-01
Source PublicationIEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
ISSN2168-2291
SubtypeArticle
Volume48Issue:2Pages:149-160
Contribution Rank1
Abstract

The ability to detect anomalies in perceived stimuli is critical to a broad range of practical and applied activities involving human operators. In this paper, we propose a real-time physiological-based system to assess the cross-task mental workload during anomaly detection. Forty participants were recruited to detect anomalous images from a set of different distracting images (Task I) and abnormal activities from surveillance videos (Task II). In Task I, the task difficulty levels were manipulated by changing the number of anomalies/distracting stimuli (15, 21, 28, or 36) with and without time constraints (i.e., 4 x 2 = 8 task difficulty levels). Physiological and behavioral data from four task difficulty levels were divided into four categories according to subjective ratings of the mental workload. The support vector machine (SVM) classifiers were trained on these data to predict the mental workload categories of: 1) the same four task difficulty levels (within level); and 2) the other four task difficulty levels in Task I (cross level). Within-level classifications (with an average of 95.29%) were more accurate than cross-level classifications (average of 72.2%), which were much more accurate than random level classifications (25%). In Task II, the same participants monitored one, two, or four video clips simultaneously in accordance with three task difficulty levels. The same physiological signals were processed for real-time recognition of a participant's mental workload after he or she completed each activity detection task. The three-class SVM classifiers were trained on physiological data from Task I to predict the mental workload categories of the Task II (cross task), achieving an overall classification accuracy of 53.83%, compared to a 33.33% accuracy at random. These results are discussed in terms of their implications for developing situation-aware recognition systems of the mental workload and adaptive human-computer interaction platforms.

KeywordAnomaly detection cross task human-computer interaction mental workload physiological measures workload classification
DOI10.1109/THMS.2018.2803025
Indexed BySCI ; SSCI
Language英语
Funding OrganizationNational Key Research and Development Plan(2016YFB1001200) ; National Natural Science Foundation of China(31771226 ; U1736220 ; 61725204 ; 61521002)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000427629300004
WOS HeadingsScience & Technology ; Technology
WOS KeywordAIR-TRAFFIC-CONTROL ; HEART-RATE ; STATE CLASSIFICATION ; DIFFICULTY ; EEG ; REHABILITATION ; RESPIRATION ; SENSITIVITY ; PERFORMANCE ; FEATURES
Citation statistics
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/26051
Collection中国科学院行为科学重点实验室
Corresponding AuthorYong-Jin Liu
Affiliation1.Inst Psychol, CAS Key Lab Behav Sci, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Tsinghua Univ, Dept Comp Sci & Technol, Tsinghua Natl Lab Informat Sci & Technol, Beijing 100044, Peoples R China
Recommended Citation
GB/T 7714
Zhao, Guozhen,Liu, Yong-Jin,Shi, Yuanchun,et al. Real-Time Assessment of the Cross-Task Mental Workload Using Physiological Measures During Anomaly Detection[J]. IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS,2018,48(2):149-160.
APA Zhao, Guozhen,Liu, Yong-Jin,Shi, Yuanchun,&Yong-Jin Liu.(2018).Real-Time Assessment of the Cross-Task Mental Workload Using Physiological Measures During Anomaly Detection.IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS,48(2),149-160.
MLA Zhao, Guozhen,et al."Real-Time Assessment of the Cross-Task Mental Workload Using Physiological Measures During Anomaly Detection".IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 48.2(2018):149-160.
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