Human–Robot Interaction through Eye Tracking for Artistic Drawing

Scalera, Lorenzo; Seriani, Stefano; Gallina, Paolo; Lentini, Mattia; Gasparetto, Alessandro

doi:10.3390/robotics10020054

Cited by 39 publications

(25 citation statements)

References 35 publications

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“…Similarly, eye tracking technology is being incorporated into virtual and augmented reality scenarios as the software for registration is included within the glasses [49][50][51]. Similarly, eye tracking technology is being incorporated into the control of industrial robots [52,53]. Finally, systems are being implemented to improve the calibration and tracking of gaze tracking for users who were previously unable to use it, due to various neurological conditions (stroke paralysis or amputations, spinal cord injuries, Parkinson's disease, multiple sclerosis, muscular dystrophy, etc.)…”

Section: Application Of the Use Of Eye Tracking Technologymentioning

confidence: 99%

Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques

et al. 2021

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In recent decades, the use of technological resources such as the eye tracking methodology is providing cognitive researchers with important tools to better understand the learning process. However, the interpretation of the metrics requires the use of supervised and unsupervised learning techniques. The main goal of this study was to analyse the results obtained with the eye tracking methodology by applying statistical tests and supervised and unsupervised machine learning techniques, and to contrast the effectiveness of each one. The parameters of fixations, saccades, blinks and scan path, and the results in a puzzle task were found. The statistical study concluded that no significant differences were found between participants in solving the crossword puzzle task; significant differences were only detected in the parameters saccade amplitude minimum and saccade velocity minimum. On the other hand, this study, with supervised machine learning techniques, provided possible features for analysis, some of them different from those used in the statistical study. Regarding the clustering techniques, a good fit was found between the algorithms used (k-means ++, fuzzy k-means and DBSCAN). These algorithms provided the learning profile of the participants in three types (students over 50 years old; and students and teachers under 50 years of age). Therefore, the use of both types of data analysis is considered complementary.

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Section: Application Of the Use Of Eye Tracking Technologymentioning

confidence: 99%

Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques

et al. 2021

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“…Other, more abstract cognitive functions, can be probed with new eye-tracking interfaces. Artistic creativity can be expressed through a human-robot interaction, in which a robotic arm is teleoperated by projected gaze movements to draw [32]. Even an intention to select an object visually can be decoded from gaze fixation features [33].…”

Section: Eye-tracking Bcis For Probing Memory and Cognitive Functionsmentioning

confidence: 99%

CyberEye: New Eye-Tracking Interfaces for Assessment and Modulation of Cognitive Functions beyond the Brain

Lech

Czyżewski

Kucewicz

2021

Sensors

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The emergence of innovative neurotechnologies in global brain projects has accelerated research and clinical applications of BCIs beyond sensory and motor functions. Both invasive and noninvasive sensors are developed to interface with cognitive functions engaged in thinking, communication, or remembering. The detection of eye movements by a camera offers a particularly attractive external sensor for computer interfaces to monitor, assess, and control these higher brain functions without acquiring signals from the brain. Features of gaze position and pupil dilation can be effectively used to track our attention in healthy mental processes, to enable interaction in disorders of consciousness, or to even predict memory performance in various brain diseases. In this perspective article, we propose the term ‘CyberEye’ to encompass emerging cognitive applications of eye-tracking interfaces for neuroscience research, clinical practice, and the biomedical industry. As CyberEye technologies continue to develop, we expect BCIs to become less dependent on brain activities, to be less invasive, and to thus be more applicable.

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“…With eye-gaze information, hand movements, and the information in virtual environment, we can predict the tasks that the user will perform. Eye-tracking systems have been found to play an increasingly important role in assistive robotics as hand-free interaction interfaces for motor-impaired people [9], social gaze control for humanoids [10], robotic guidance [11], creating artistic drawings [12], and safe robot interactions in patients with speech and motor impairments [13]. Eyetracking combined with action observation tasks in a virtual reality display has been used to monitor motor deficits derived from stroke and, consequently, for the rehabilitation of stroke patients [9,14].…”

Section: Introductionmentioning

confidence: 99%

Improving Haptic Response for Contextual Human Robot Interaction

Mugisha

Guda

Chevallereau

et al. 2022

Sensors

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For haptic interaction, a user in a virtual environment needs to interact with proxies attached to a robot. The device must be at the exact location defined in the virtual environment in time. However, due to device limitations, delays are always unavoidable. One of the solutions to improve the device response is to infer human intended motion and move the robot at the earliest time possible to the desired goal. This paper presents an experimental study to improve the prediction time and reduce the robot time taken to reach the desired position. We developed motion strategies based on the hand motion and eye-gaze direction to determine the point of user interaction in a virtual environment. To assess the performance of the strategies, we conducted a subject-based experiment using an exergame for reach and grab tasks designed for upper limb rehabilitation training. The experimental results in this study revealed that eye-gaze-based prediction significantly improved the detection time by 37% and the robot time taken to reach the target by 27%. Further analysis provided more insight on the effect of the eye-gaze window and the hand threshold on the device response for the experimental task.

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Human–Robot Interaction through Eye Tracking for Artistic Drawing

Cited by 39 publications

References 35 publications

Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques

Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques

CyberEye: New Eye-Tracking Interfaces for Assessment and Modulation of Cognitive Functions beyond the Brain

Improving Haptic Response for Contextual Human Robot Interaction

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