Does User's Prior Knowledge Worth Consideration?: The Influence of Prior Knowledge Toward Mutual Gaze Convergence

Thepsoonthorn, Chidchanok; Yokozuka, Takahiro; Kwon, Jinhwan; Yap, Robin Mia Sin; Miura, Shunsuke; Ogawa, Kazuhiko; Miyake, Yoichi

doi:10.1109/compsac.2016.99

Cited by 1 publication

(4 citation statements)

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“…The influence of the internal state, particularly prior knowledge, on mutual gaze convergence between two persons was demonstrated during a lecture assignment with and without prior knowledge interaction. The main goal was to examine the differences in gaze behaviour between the two scenarios and determine whether prior knowledge directly impacts human gaze behaviour [36]. A recognition classifier for FER in assessing teaching skill (ATS) system was developed using CNN, modified CNN [31] and SVM [37].…”

Section: Face-basedmentioning

confidence: 99%

“…Their details were removed before the video files were analysed. [28], [86][103], [29], [36], [32], [96], [79], [8], [18], [84], [87], [101], [25], [102] Real-time recognition dataset…”

Section: Primarymentioning

confidence: 99%

“…The study of human internal state elements, such as prior knowledge, which influence the behaviour of users, is still in its early stages. Prior knowledge influences an individual"s gaze behaviours, such as mutual gaze convergence between two people during a lecture task, with two conditions: interaction with and without prior knowledge [36].…”

Section: Challenges Related To Human-machinementioning

confidence: 99%

“…Moreover, further research on cultural and gender differences is needed to obtain a deep understanding of human behaviour variation and its potential impacts [64] [36]. These studies are crucial for improving the effectiveness and adaptability of usercentred adaptive systems [36].…”

Section: 421mentioning

confidence: 99%

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Robotics Utilization in Automatic Vision-Based Assessment Systems From Artificial Intelligence Perspective: A Systematic Review

et al. 2022

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This study presents a systematic review of the automatic vision-based assessment systems and models from related fields over the last 6 years. Many studies focus on automatic vision-based assessment in the area of robotics, artificial intelligence and virtual reality or augmented reality (AR) to enhance the communication between humans and machines for general purposes. Three reliable databases, IEEEXplore, Science Direct and Web of Science, were used to obtain relevant studies on the given topic. Several stages of filtering and scanning were applied according to the inclusion/exclusion criteria to filter the obtained 3505 papers from 2015 to 2020. Finally, 97 papers met the criteria. They were classified into four main categories based on their field by following the scientific taxonomy: computer-vision-based, robotics-based, AR-based and hybrid-based categories, accounting for 42.26% (n = 41/97 papers), 48.45% (n = 47/97 papers), 6.18% (n = 6/97 papers) and 4.12% (n = 4/97 papers), respectively. Subsequently, a deep and critical analysis of this multifield systematic review highlighted new research opportunities, motivations, challenges and recommendations that need attention to integrate interdisciplinary studies. Thus, automatic vision-based assessment, which is a field requiring automated solutions, tools and methods, enhances the ability of the assistive technology and facilitates the interaction of individuals with machines. Many studies have been conducted on the automatic vision-based assessment systems and their subtypes to promote accurate communication and performance evaluation in human-machine interaction. This study can provide researchers with useful guides and valuable information for future research. This study also addresses the ambiguity of automatic vison-based assessment models in interdisciplinary fields.

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Section: Face-basedmentioning

confidence: 99%

“…Their details were removed before the video files were analysed. [28], [86][103], [29], [36], [32], [96], [79], [8], [18], [84], [87], [101], [25], [102] Real-time recognition dataset…”

Section: Primarymentioning

confidence: 99%

Section: Challenges Related To Human-machinementioning

confidence: 99%

Section: 421mentioning

confidence: 99%

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