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NON-VERBAL HUMAN-ROBOT INTERACTION USING NEURAL NETWORK FOR THE APPLICATION OF SERVICE ROBOT

Service robots ; re prevailing in many industries to assis.: humans in c..md acing repetitive tasks, which require a natural interaction called Human Robot Interaction (HRI). In particular. nonverbal HRI plays an important role in social interactions, which highlights the need to accurately detect t...

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Main Authors: SOOMRO, ZUBAIR ADEL, SHAMSUDIN, ABU UBAIDAH, ABDUL RAHIM, RUZAIRI, ADRIANSHAH, ANDI, MOHD HAZELI, MOHD HAZELI
Format: Article
Language:English
Published: 2023
Subjects:
T Technology (General)
Online Access:http://eprints.uthm.edu.my/9285/1/J15662_942c3ecbc3be675cdaa9744d7645b4b4.pdf
http://eprints.uthm.edu.my/9285/
https://doi.org/10.314361iiumej v24i.2577
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spelling my.uthm.eprints.92852023-07-17T07:47:48Z http://eprints.uthm.edu.my/9285/ NON-VERBAL HUMAN-ROBOT INTERACTION USING NEURAL NETWORK FOR THE APPLICATION OF SERVICE ROBOT SOOMRO, ZUBAIR ADEL SHAMSUDIN, ABU UBAIDAH ABDUL RAHIM, RUZAIRI ADRIANSHAH, ANDI MOHD HAZELI, MOHD HAZELI T Technology (General) Service robots ; re prevailing in many industries to assis.: humans in c..md acing repetitive tasks, which require a natural interaction called Human Robot Interaction (HRI). In particular. nonverbal HRI plays an important role in social interactions, which highlights the need to accurately detect the subject's attention by evaluating the programmed cues. In this paper, a conceptual attentiveness model algorithm called attentive Recognition Model (ARM) is proposed to recognize a person's aii:ontiveness, which improves the of detection and subjective experience during nonverbal ARI using three combined detection models: face tracking, iris tracking and eye blinking. The face tracking model was trained using a Long Short-Term Memory (LSTM) neural network, which is based on deep learning. Meanwhile, the iris tracking and eye blinking use a mathematical model. The eye blinking model uses a random face landmark point to calculate the Eye Aspect Ratio (EAR), which is much more reliable compared to the prior method, which could detect a person blinking at a further distance even if the person was not blinking, The conducted experiments for face and iris tracking were able to detect direction up to 2 meters. Meanwhile, the tested eye blinking model gave an accuracy of 83.33% at up to 2 meters, The overall attentive accuracy of ARM was up to 85.7%. The experiments showed that the service robot was able to understand the programmed cues and hence perform certain tasks, such as approaching the interested person. 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/9285/1/J15662_942c3ecbc3be675cdaa9744d7645b4b4.pdf SOOMRO, ZUBAIR ADEL and SHAMSUDIN, ABU UBAIDAH and ABDUL RAHIM, RUZAIRI and ADRIANSHAH, ANDI and MOHD HAZELI, MOHD HAZELI (2023) NON-VERBAL HUMAN-ROBOT INTERACTION USING NEURAL NETWORK FOR THE APPLICATION OF SERVICE ROBOT. HUM Engineering Journal, 24 (1). pp. 301-318. https://doi.org/10.314361iiumej v24i.2577
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
SOOMRO, ZUBAIR ADEL
SHAMSUDIN, ABU UBAIDAH
ABDUL RAHIM, RUZAIRI
ADRIANSHAH, ANDI
MOHD HAZELI, MOHD HAZELI
NON-VERBAL HUMAN-ROBOT INTERACTION USING NEURAL NETWORK FOR THE APPLICATION OF SERVICE ROBOT
description Service robots ; re prevailing in many industries to assis.: humans in c..md acing repetitive tasks, which require a natural interaction called Human Robot Interaction (HRI). In particular. nonverbal HRI plays an important role in social interactions, which highlights the need to accurately detect the subject's attention by evaluating the programmed cues. In this paper, a conceptual attentiveness model algorithm called attentive Recognition Model (ARM) is proposed to recognize a person's aii:ontiveness, which improves the of detection and subjective experience during nonverbal ARI using three combined detection models: face tracking, iris tracking and eye blinking. The face tracking model was trained using a Long Short-Term Memory (LSTM) neural network, which is based on deep learning. Meanwhile, the iris tracking and eye blinking use a mathematical model. The eye blinking model uses a random face landmark point to calculate the Eye Aspect Ratio (EAR), which is much more reliable compared to the prior method, which could detect a person blinking at a further distance even if the person was not blinking, The conducted experiments for face and iris tracking were able to detect direction up to 2 meters. Meanwhile, the tested eye blinking model gave an accuracy of 83.33% at up to 2 meters, The overall attentive accuracy of ARM was up to 85.7%. The experiments showed that the service robot was able to understand the programmed cues and hence perform certain tasks, such as approaching the interested person.
format Article
author SOOMRO, ZUBAIR ADEL
SHAMSUDIN, ABU UBAIDAH
ABDUL RAHIM, RUZAIRI
ADRIANSHAH, ANDI
MOHD HAZELI, MOHD HAZELI
author_facet SOOMRO, ZUBAIR ADEL
SHAMSUDIN, ABU UBAIDAH
ABDUL RAHIM, RUZAIRI
ADRIANSHAH, ANDI
MOHD HAZELI, MOHD HAZELI
author_sort SOOMRO, ZUBAIR ADEL
title NON-VERBAL HUMAN-ROBOT INTERACTION USING NEURAL NETWORK FOR THE APPLICATION OF SERVICE ROBOT
title_short NON-VERBAL HUMAN-ROBOT INTERACTION USING NEURAL NETWORK FOR THE APPLICATION OF SERVICE ROBOT
title_full NON-VERBAL HUMAN-ROBOT INTERACTION USING NEURAL NETWORK FOR THE APPLICATION OF SERVICE ROBOT
title_fullStr NON-VERBAL HUMAN-ROBOT INTERACTION USING NEURAL NETWORK FOR THE APPLICATION OF SERVICE ROBOT
title_full_unstemmed NON-VERBAL HUMAN-ROBOT INTERACTION USING NEURAL NETWORK FOR THE APPLICATION OF SERVICE ROBOT
title_sort non-verbal human-robot interaction using neural network for the application of service robot
publishDate 2023
url http://eprints.uthm.edu.my/9285/1/J15662_942c3ecbc3be675cdaa9744d7645b4b4.pdf
http://eprints.uthm.edu.my/9285/
https://doi.org/10.314361iiumej v24i.2577
_version_ 1772813824103022592
score 13.211869

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