Classification of gaze preference decision for human-machine interaction using eye tracking device

Shimizu, Sota; Tanzawa, Yoshiaki; Hashizume, Takumi

doi:10.1504/ijma.2012.048183

Cited by 3 publications

(3 citation statements)

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“…Cognitive workload of an air traffic controller has been investigated in [3] using Functional Near-Infrared spectroscopy with the findings that accuracy and speed of a pilot decreased and blood oxygenation increased with the increase in task difficulty. Eye tracking has been also used in studying different aspects of human-machine interaction [15,14]. In this paper, the authors attempt to address some of the limitations found in mathematical models of the human pilot by using eye tracking data to provide insight into the way the pilot uses the available information.…”

Section: Introductionmentioning

confidence: 99%

Understanding Human Behaviour in Flight Operation Using Eye-Tracking Technology

Korek

Mendez

Asad

et al. 2020

Engineering Psychology and Cognitive Ergonomics. Cognition and Design

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A clear understanding of how the pilot processes the information in the cockpit while carrying out particular tasks is crucial for developing the Human-Machine Interface and inceptors that help reduce pilot workload. Eye-tracking data synchronised with aircraft dynamics data is used here to study the high-workload scenario of executing an offset landing in an engineering flight simulator. The study focused on identifying differences in behavioural patterns between line pilots and test pilots. Evidence for significant differences were found regarding the ability to multitask and monitor aircraft states. The research output will lead to reduction of the pilot's workload and, in further study, proposition of a new display setups and inceptors.

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Section: Introductionmentioning

confidence: 99%

Understanding Human Behaviour in Flight Operation Using Eye-Tracking Technology

Korek

Mendez

Asad

et al. 2020

Engineering Psychology and Cognitive Ergonomics. Cognition and Design

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“…In [2], Oborski deals with some problems of overall system performance in man-machine interactions and with an attempt to model human behavior to classify knowledge regarding man-machine interactions. Shimizu and Hashizume [3] used an eye tracking device to classify gaze preference decision for HRI. All of these researchers analyzed the preference by control scheme or image process and then applied intelligent computing to generate the decision for the HRI.…”

Section: Literature Reviewmentioning

confidence: 99%

3D hand posture estimation and task semantic monitoring technique for human-robot collaboration

Wang

et al. 2013

2013 IEEE International Conference on Mechatronics and Automation

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We develop a monitoring system for humanrobot collaboration (HRC) of hybrid automation. The system enables the production line to avoid product defects as well as guarantee a real-time warning system in a cellular manufacturing (CM). A task semantic monitoring system includes the three-dimensional (3D) hand posture estimation and the motion trajectory monitoring. The coordinate of hand, the motion of hand, and the semantic of hand are identified by two integrated modules. The verification is executed by applying the system to the assembly lines for power protector.

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“…It is supposed to be quite essential that we make an effective use of knowledge from observation of the human eye movement, in order not only to develop the human-friendly robots having the high affinity with the human users but also to contribute to advance of product design on assumption of man-machine cooperation. Some unique studies on measuring and analyzing eye movement have been reported [7][8] [11] [12]. One study regards the human eye movement as a kind of motion skill and challenges to apply it for improving robot performance [7].…”

Section: Introductionmentioning

confidence: 99%

Development of Air Hockey Robot improving with the human players

Ogawa

Shimizu

Kadogawa

et al. 2011

IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society

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This paper presents a developed Air Hockey Robot system, comprising camera unit, processing unit, and arm unit. This system performs as (1) detecting position of an Air Hockey puck from images captured using a ceil camera, (2) generating a 2-axis robot arm trajectory and (3) controlling the arm in order to shoot the puck to an opponent human player. Thus, the processing unit needs to execute three processes, i.e., image processing, trajectory generation, and arm control process. In this paper, we pay the most attention to trajectory generation process for improving the robot offensive performance. We give the robot a skill as attacking a weak point of human vision mechanism with respect to trajectory generation of the robot arm when it shoots stationary puck. In other words, the human player needs to change gaze points between the robot hand and the moving puck because the human can observe any object finely only in their central field of view, corresponding to around a fovea in spite of their quite wide field of view. Thus, we intend to plan the arm trajectory generation and control the arm as more easily the human players has a mistake caused by a time delay occurring when the human players switch their gaze point. Verification experiments, of changing speed of the puck shot from the same arm trajectory as the human players' mistake is caused easily, have been conducted using the developed Air Hockey Robot system. I.

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Classification of gaze preference decision for human-machine interaction using eye tracking device

Cited by 3 publications

References 0 publications

Understanding Human Behaviour in Flight Operation Using Eye-Tracking Technology

Understanding Human Behaviour in Flight Operation Using Eye-Tracking Technology

3D hand posture estimation and task semantic monitoring technique for human-robot collaboration

Development of Air Hockey Robot improving with the human players

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