國立虎尾科技大學 |

Inferring Mental Workload Changes of Subjects Unfamiliar with a Touch Screen Game through Physiological and Behavioral Measurements.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Inferring Mental Workload Changes of Subjects Unfamiliar with a Touch Screen Game through Physiological and Behavioral Measurements./
作者:	Parsinejad, Payam.
面頁冊數:	1 online resource (172 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-02(E), Section: B.
Contained By:	Dissertation Abstracts International78-02B(E).
標題:	Mechanical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9781369016932

Inferring Mental Workload Changes of Subjects Unfamiliar with a Touch Screen Game through Physiological and Behavioral Measurements.
Parsinejad, Payam.

Inferring Mental Workload Changes of Subjects Unfamiliar with a Touch Screen Game through Physiological and Behavioral Measurements. - 1 online resource (172 pages)

Source: Dissertation Abstracts International, Volume: 78-02(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Many tasks can be demanding for human operators, including operating an aircraft, driving a vehicle, and making decisions in an air traffic control setting. These tasks, depending on their complexity, cause increased mental workload on humans, which could then lead to human errors. Understanding the interaction dynamics between the human operators and tasks requires effectively detecting and carefully evaluating human mental states. If done successfully, this would help design ways by which the machine can infer mental states and respond intelligently to the operator in a way to assist with the objective to reduce the probability of human error in a task.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369016932Subjects--Topical Terms:

557493
Mechanical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Inferring Mental Workload Changes of Subjects Unfamiliar with a Touch Screen Game through Physiological and Behavioral Measurements.
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245 1 0 $a Inferring Mental Workload Changes of Subjects Unfamiliar with a Touch Screen Game through Physiological and Behavioral Measurements.
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500 $a Source: Dissertation Abstracts International, Volume: 78-02(E), Section: B.
500 $a Adviser: Rifat Sipahi.
502 $a Thesis (Ph.D.) $c Northeastern University $d 2016.
504 $a Includes bibliographical references
520 $a Many tasks can be demanding for human operators, including operating an aircraft, driving a vehicle, and making decisions in an air traffic control setting. These tasks, depending on their complexity, cause increased mental workload on humans, which could then lead to human errors. Understanding the interaction dynamics between the human operators and tasks requires effectively detecting and carefully evaluating human mental states. If done successfully, this would help design ways by which the machine can infer mental states and respond intelligently to the operator in a way to assist with the objective to reduce the probability of human error in a task.
520 $a Even if the operators are experts in many tasks, when they are faced with a challenging situation they are not familiar with, then their task execution may not be perfect and human error may still be inevitable. To understand this phenomenon and design an inference scheme to detect it via a machine, a touch-screen air traffic management game is designed with two unique difficulty levels, easy and difficult, requiring different mental workload levels. While volunteering subjects are trained and are hence familiar with the easy level of the game, they are only knowledgeable of the difficult game without any training experience.
520 $a Two main results of this dissertation are as follows: (a) Outcome of the experiments indicates that data collected from subjects' heart rate and skin conductance as well as subjects' finger-stroke patterns on the touch screen can all help flag unfamiliarity of subjects in the difficult game. (b) Subjects' behavioral patterns are used to create models using machine learning techniques, whereby the models can autonomously predict the game difficulty solely by tracking subjects' movements in real time. Experiments with newly recruited subjects indicate that such models can indeed predict what game difficulty the subjects are encountering, even if we have no priori knowledge of the game level.
520 $a Results obtained in this dissertation point out many future opportunities in synergistic human-machine systems, and pave the way toward real-time adaptive machines that can perform inferences to evaluate the probability of a human error in critical tasks, and can in turn provide a set of assistance modalities to the humans, with the aim to minimize such errors.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Mechanical engineering. $3 557493
650 4 $a Industrial engineering. $3 679492
650 4 $a Computer science. $3 573171
650 4 $a Cognitive psychology. $3 556029
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710 2 $a Northeastern University. $b Mechanical and Industrial Engineering. $3 845717
773 0 $t Dissertation Abstracts International $g 78-02B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10146333 $z click for full text (PQDT)