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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 | |
第一作者 | Zhao, Guozhen |
通讯作者邮箱 | liuyongjin@tsinghua.edu.cn |
心理所单位排序 | 1 |
摘要 | 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. |
关键词 | Anomaly detection cross task human-computer interaction mental workload physiological measures workload classification |
2018-04-01 | |
DOI | 10.1109/THMS.2018.2803025 |
发表期刊 | IEEE Transactions on Human-Machine Systems |
ISSN | 2168-2291 |
卷号 | 48期号:2页码:149-160 |
收录类别 | SCI |
WOS关键词 | AIR-TRAFFIC-CONTROL ; HEART-RATE ; STATE CLASSIFICATION ; DIFFICULTY ; EEG ; REHABILITATION ; RESPIRATION ; SENSITIVITY ; PERFORMANCE ; FEATURES |
WOS标题词 | Science & Technology ; Technology |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
WOS记录号 | WOS:000427629300004 |
WOS分区 | Q2 |
Q分类 | Q2 |
资助机构 | National Key Research and Development Plan(2016YFB1001200) ; National Natural Science Foundation of China(31771226 ; U1736220 ; 61725204 ; 61521002) |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.psych.ac.cn/handle/311026/26051 |
专题 | 中国科学院行为科学重点实验室 |
通讯作者 | Liu, Yong-Jin |
作者单位 | 1.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 |
第一作者单位 | 中国科学院行为科学重点实验室 |
推荐引用方式 GB/T 7714 | Zhao, Guozhen,Liu, Yong-Jin,Shi, Yuanchun. 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.(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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Real-Time Assessment(688KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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