Attention Estimation for Child-Robot Interaction

Attamimi, Muhammad; Miyata, Masahiro; Yamada, Tetsuji; Omori, Takashi; Hida, Ryoma

doi:10.1145/2974804.2980510

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…Researchers also try to measure attention with different modalities. For example, in addition to collect gaze direction, some studies are based on reaction time [15], facial expression, user's physical distance with the robot [29,30], general behavior comprising verbal and non-verbal behavior [31], posture, as well as ongoing activities [28].…”

Section: Control and Measure Of Attention-distractionmentioning

confidence: 99%

MemoRob: Studying Robots Distractor Effects

Jost¹,

Pévédic²,

Grandgeorge³

et al. 2020

MMC_C

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MemoRob is a model about how to optimize the use of robots for learning. It is based on a list of each possible robotic source of distraction associated with its relevant effects according to its nature and to the target learning mode. While collecting the sources of distraction that the robotics literature provides, the instantiation method of pairing each source and each learning mode with the nature of the distraction as well as its distracting effects allows to consider how to remedy these effects of robotic distraction effects although still having the robotic input as a learning medium. In this article, we provide the motivations that led to the need for the MemoRob model, the list of sources and effects generated by the Human-Robot interaction that may interfere with learning situations, the learning modes described according to their processes and mechanisms and, finally, a set of predictions on whether a given robotic learning situations might promote attention or distraction.

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Section: Control and Measure Of Attention-distractionmentioning

confidence: 99%

MemoRob: Studying Robots Distractor Effects

Jost¹,

Pévédic²,

Grandgeorge³

et al. 2020

MMC_C

View full text Add to dashboard Cite

show abstract

“…Visual attention in human-robot interaction has been widely studied because its crucial but, it is often evaluated using external devices, such as high-resolution cameras and/or kinetics, e.g., [46,47], due to the limited video recording resources on the most common commercial robotic platforms for social applications. Authors proposed a system for robot-assisted therapy that employs five individual sensors, including three RGB cameras and two Microsoft Kinects [18].…”

Section: Face Detection and Alignment For Estimating The Visual Focusmentioning

confidence: 99%

Deep Learning Systems for Estimating Visual Attention in Robot-Assisted Therapy of Children with Autism and Intellectual Disability

et al. 2018

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Recent studies suggest that some children with autism prefer robots as tutors for improving their social interaction and communication abilities which are impaired due to their disorder. Indeed, research has focused on developing a very promising form of intervention named Robot-Assisted Therapy. This area of intervention poses many challenges, including the necessary flexibility and adaptability to real unconstrained therapeutic settings, which are different from the constrained lab settings where most of the technology is typically tested. Among the most common impairments of children with autism and intellectual disability is social attention, which includes difficulties in establishing the correct visual focus of attention. This article presents an investigation on the use of novel deep learning neural network architectures for automatically estimating if the child is focusing their visual attention on the robot during a therapy session, which is an indicator of their engagement. To study the application, the authors gathered data from a clinical experiment in an unconstrained setting, which provided low-resolution videos recorded by the robot camera during the child-robot interaction. Two deep learning approaches are implemented in several variants and compared with a standard algorithm for face detection to verify the feasibility of estimating the status of the child directly from the robot sensors without relying on bulky external settings, which can distress the child with autism. One of the proposed approaches demonstrated a very high accuracy and it can be used for off-line continuous assessment during the therapy or for autonomously adapting the intervention in future robots with better computational capabilities.

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“…One of the ability in visual recognition is to recognize the facial expressions of a person during the interaction. Generally, non-verbal information such as body movements, gestures, facial expressions; takes a great role in a face-to-face interaction including communication [16][17][18]. Moreover, the facial expression is strongly related to human emotion that indicates the inner state of the speaker and/or the listener during communication.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based facial expressions recognition system for assisting visually impaired persons

2020

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In general, a good interaction including communication can be achieved when verbal and non-verbal information such as body movements, gestures, facial expressions, can be processed in two directions between the speaker and listener. Especially the facial expression is one of the indicators of the inner state of the speaker and/or the listener during the communication. Therefore, recognizing the facial expressions is necessary and becomes the important ability in communication. Such ability will be a challenge for the visually impaired persons. This fact motivated us to develop a facial recognition system. Our system is based on deep learning algorithm. We implemented the proposed system on a wearable device which enables the visually impaired persons to recognize facial expressions during the communication. We have conducted several experiments involving the visually impaired persons to validate our proposed system and the promising results were achieved.

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Attention Estimation for Child-Robot Interaction

Cited by 6 publications

References 3 publications

MemoRob: Studying Robots Distractor Effects

MemoRob: Studying Robots Distractor Effects

Deep Learning Systems for Estimating Visual Attention in Robot-Assisted Therapy of Children with Autism and Intellectual Disability

Deep learning based facial expressions recognition system for assisting visually impaired persons

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