A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level

Chakraborty, Partha; Ahmed, Shamsuddin; Yousuf, Mohammad Abu; Azad, A. K. M.; Alyami, Salem A.; Moni, Mohammad Ali

doi:10.1109/access.2021.3091642

Cited by 34 publications

(12 citation statements)

References 40 publications

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“…The assumption was that the person had no attention if he was always seeing something. The suggested ML approaches' validation and test accuracy were close to 99.24% and 99.43%, respectively [45].…”

Section: P B Nithin Et Al (mentioning

confidence: 74%

The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

Uddin

Moshayedi

et al. 2022

EAI Endorsed Trans AI Robotics

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Facial recognition research is one of the different types of research in this world today. In recent years, facial recognition in robots has attracted increased study interest. Robotic platforms now utilize a variety of object detection methods, with face detection being a viable use. Face detection in robotics is a computer technique that recognizes human faces in digital pictures and is used in a range of applications. Different authors have performed their research in different ways on the use of detection systems. This paper aims to give future researchers a better idea of using facial recognition systems in robotics. In this study, we reviewed research by various authors over recent years to facilitate future facial recognition research. In addition, scholars have addressed the topics, how they have done so, and the specifics of their approaches are described. This paper reviewed an overview of hardware implementation and software implementation by various authors. It can automatically focus cameras or count the number of people who have entered a location. Commercial applications of the method include displaying tailored advertisements in response to a recognized face along with the algorithms, functions and architectures used in facial recognition and giving the opinions of various authors mentioned. The comparative analysis of facial recognition and its architecture system is highlighted.

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Section: P B Nithin Et Al (mentioning

confidence: 74%

The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

Uddin

Moshayedi

et al. 2022

EAI Endorsed Trans AI Robotics

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“…They encompass intention prediction, [30][31][32] motion prediction, [33][34][35][36][37][38][39] and attention estimation. [40,41] Beyond collaborative tasks, human activity prediction holds efficacy for surveillance systems and social HRI.…”

Section: Human Activitymentioning

confidence: 99%

Multimodal Human–Robot Interaction for Human‐Centric Smart Manufacturing: A Survey

Wang,

Zheng,

et al. 2023

Advanced Intelligent Systems

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Human–robot interaction (HRI) has escalated in notability in recent years, and multimodal communication and control strategies are necessitated to guarantee a secure, efficient, and intelligent HRI experience. In spite of the considerable focus on multimodal HRI, comprehensive disquisitions delineating various modalities and intricately analyzing their combinations remain elusive, consequently limiting holistic understanding and future advancements. This article aspires to bridge this inadequacy by conducting a profound exploration of multimodal HRI, predominantly concentrating on four principal modalities: vision, auditory and language, haptics, and physiological sensing. An extensive review encapsulating algorithmic dissection, interface devices, and applicative dimensions forms part of this discourse. This manuscript distinctively combines multimodal HRI with cognitive science, deeply probing into the three dimensions, perception, cognition, and action, thereby demystifying algorithms intrinsic to multimodal HRI. Finally, it accentuates the empirical challenges and contours preemptive trajectories for multimodal HRI in human‐centric smart manufacturing.

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“…The direction of the head is usually one of the determining aspects of whether the interlocutor is heedful, whether it's between two humans or between a human and a machine [60][61][62][63][64].…”

Section: Data Collection Processmentioning

confidence: 99%

Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset

Napoli¹,

Russo²,

Iocchi³

et al. 2022

OBM Neurobiol

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Human–robot interactions require the ability of the system to determine if the user is paying attention. However, to train such systems, massive amounts of data are required. In this study, we addressed the issue of data scarcity by constructing a large dataset (containing ~120,000 photographs) for the attention detection task. Then, by using this dataset, we established a powerful baseline system. In addition, we extended the proposed system by adding an auxiliary face detection module and introducing a unique GAN-based data augmentation technique. Experimental results revealed that the proposed system yields superior performance compared to baseline models and achieves an accuracy of 88% on the test set. Finally, we created a web application for testing the proposed model in real time.

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A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level

Cited by 34 publications

References 40 publications

The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

Multimodal Human–Robot Interaction for Human‐Centric Smart Manufacturing: A Survey

Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset

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