2021
DOI: 10.3390/healthcare9070894
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Impacts of Human Robot Proxemics on Human Concentration-Training Games with Humanoid Robots

Abstract: The use of humanoid robots within a therapeutic role, that is, helping individuals with social disorders, is an emerging field, but it remains unexplored in terms of concentration training. To seamlessly integrate humanoid robots into concentration games, an investigation into the impacts of human robot interactive proxemics on concentration-training games is particularly important. In the case of an epidemic diffusion especially—for example, during the COVID-19 pandemic—HRI games may help in the therapeutic p… Show more

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Cited by 5 publications
(4 citation statements)
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“…Combining gestures and speech, as suggested in [39], enhances the robot's engagement and naturalness, making it more appealing and effective. In [60] the potential for improving human-robot interaction by modulating robot behavior to express different moods was also indicated.…”
Section: Main Empirical Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…Combining gestures and speech, as suggested in [39], enhances the robot's engagement and naturalness, making it more appealing and effective. In [60] the potential for improving human-robot interaction by modulating robot behavior to express different moods was also indicated.…”
Section: Main Empirical Resultsmentioning
confidence: 99%
“…Studies have explored physical and psychological distances between humans and robots in various contexts, such as movement within a room. The extent and speed of movements, as well as distances during interactions, have been scrutinized to understand how these factors affect communication and engagement [42,60].…”
Section: Types Of Nonverbal Communication Examinedmentioning
confidence: 99%
“…In the experiments, we adopted HRI games based on gesture recognition. The gesture recognition algorithm consisted of network training and gesture recognition [ 37 ]. In the network training stage, the improved feature extraction network was used to fit the feature distribution of training samples, and the fitting error was estimated according to the deviation between the gesture label and the network output; that is, the recognition result and the updating direction of network weights at all levels were determined by calculating the error gradient.…”
Section: Resultsmentioning
confidence: 99%
“…The effect of these factors on the training effectiveness of the concentration game was analyzed. We specifically discussed the impact of physical distance and orientation on gesture recognition accuracy in our previously published paper [ 37 ]. In order to compare and analyze the data, we performed Z-Score normalization on the children’s game scores, which are shown in Figure 6 and Figure 7 .…”
Section: Resultsmentioning
confidence: 99%