PSYCH OpenIR  > 中国科学院行为科学重点实验室
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
发表期刊IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
ISSN2168-2291
文章类型Article
卷号48期号:2页码:149-160
产权排序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
DOI10.1109/THMS.2018.2803025
收录类别SCI ; SSCI
语种英语
项目资助者National Key Research and Development Plan(2016YFB1001200) ; National Natural Science Foundation of China(31771226 ; U1736220 ; 61725204 ; 61521002)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号WOS:000427629300004
WOS标题词Science & Technology ; Technology
关键词[WOS]AIR-TRAFFIC-CONTROL ; HEART-RATE ; STATE CLASSIFICATION ; DIFFICULTY ; EEG ; REHABILITATION ; RESPIRATION ; SENSITIVITY ; PERFORMANCE ; FEATURES
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Yong-Jin Liu
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
推荐引用方式
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.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
Real-Time Assessment(688KB)期刊论文出版稿限制开放CC BY-NC-SA浏览 请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Zhao, Guozhen]的文章
[Liu, Yong-Jin]的文章
[Shi, Yuanchun]的文章
百度学术
百度学术中相似的文章
[Zhao, Guozhen]的文章
[Liu, Yong-Jin]的文章
[Shi, Yuanchun]的文章
必应学术
必应学术中相似的文章
[Zhao, Guozhen]的文章
[Liu, Yong-Jin]的文章
[Shi, Yuanchun]的文章
相关权益政策
暂无数据
收藏/分享
文件名: Real-Time Assessment of the Cross-Task Mental Workload Using Physiological Measures During Anomaly Detection.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。