Computer Vision for Human-Computer Interaction Using Noninvasive Technology

Computer vision is a significant component of human-computer interaction (HCI) processes in interactive control systems. In general, the interaction between humans and computers relies on the flexibility of the interactive visualization system. Electromyography (EMG) is a bioelectric signal used in...

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Autores principales:	Janarthanan Ramadoss, J. Venkatesh, Shubham Joshi, Piyush Kumar Shukla, Sajjad Shaukat Jamal, Majid Altuwairiqi, Basant Tiwari
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/b18c3215adb9429ea61978187943b8e5
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spelling	oai:doaj.org-article:b18c3215adb9429ea61978187943b8e52021-11-15T01:19:53ZComputer Vision for Human-Computer Interaction Using Noninvasive Technology1875-919X10.1155/2021/3902030https://doaj.org/article/b18c3215adb9429ea61978187943b8e52021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3902030https://doaj.org/toc/1875-919XComputer vision is a significant component of human-computer interaction (HCI) processes in interactive control systems. In general, the interaction between humans and computers relies on the flexibility of the interactive visualization system. Electromyography (EMG) is a bioelectric signal used in HCI that can be captured noninvasively by placing electrodes on the human hand. Due to the impact of complex background, accurate recognition and analysis of human motion in real-time multitarget scenarios are considered challenging in HCI. Further, EMG signals of human hand motions are exceedingly nonlinear, and it is important to utilize a dynamic approach to address the noise problem in EMG signals. Hence, in this paper, the Optimized Noninvasive Human-Computer Interaction (ONIHCI) model has been proposed to predict human motion recognition. Average Intrinsic Mode Function (AIMF) has been used to reduce the noise factor in EMG signals. Furthermore, this paper introduces spatial thermographic imaging to overcome the conventional sensor problem, such as gesture recognition and human target identification in multitarget scenarios. The human motion behavior in spatial thermographic images is examined by target trajectory, and body movement kinematics is employed to classify human targets and objects. The experimental findings demonstrate that the proposed method reduces noise by 7.2% and improves accuracy by 97.2% in human motion recognition and human target identification.Janarthanan RamadossJ. VenkateshShubham JoshiPiyush Kumar ShuklaSajjad Shaukat JamalMajid AltuwairiqiBasant TiwariHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Janarthanan Ramadoss J. Venkatesh Shubham Joshi Piyush Kumar Shukla Sajjad Shaukat Jamal Majid Altuwairiqi Basant Tiwari Computer Vision for Human-Computer Interaction Using Noninvasive Technology
description	Computer vision is a significant component of human-computer interaction (HCI) processes in interactive control systems. In general, the interaction between humans and computers relies on the flexibility of the interactive visualization system. Electromyography (EMG) is a bioelectric signal used in HCI that can be captured noninvasively by placing electrodes on the human hand. Due to the impact of complex background, accurate recognition and analysis of human motion in real-time multitarget scenarios are considered challenging in HCI. Further, EMG signals of human hand motions are exceedingly nonlinear, and it is important to utilize a dynamic approach to address the noise problem in EMG signals. Hence, in this paper, the Optimized Noninvasive Human-Computer Interaction (ONIHCI) model has been proposed to predict human motion recognition. Average Intrinsic Mode Function (AIMF) has been used to reduce the noise factor in EMG signals. Furthermore, this paper introduces spatial thermographic imaging to overcome the conventional sensor problem, such as gesture recognition and human target identification in multitarget scenarios. The human motion behavior in spatial thermographic images is examined by target trajectory, and body movement kinematics is employed to classify human targets and objects. The experimental findings demonstrate that the proposed method reduces noise by 7.2% and improves accuracy by 97.2% in human motion recognition and human target identification.
format	article
author	Janarthanan Ramadoss J. Venkatesh Shubham Joshi Piyush Kumar Shukla Sajjad Shaukat Jamal Majid Altuwairiqi Basant Tiwari
author_facet	Janarthanan Ramadoss J. Venkatesh Shubham Joshi Piyush Kumar Shukla Sajjad Shaukat Jamal Majid Altuwairiqi Basant Tiwari
author_sort	Janarthanan Ramadoss
title	Computer Vision for Human-Computer Interaction Using Noninvasive Technology
title_short	Computer Vision for Human-Computer Interaction Using Noninvasive Technology
title_full	Computer Vision for Human-Computer Interaction Using Noninvasive Technology
title_fullStr	Computer Vision for Human-Computer Interaction Using Noninvasive Technology
title_full_unstemmed	Computer Vision for Human-Computer Interaction Using Noninvasive Technology
title_sort	computer vision for human-computer interaction using noninvasive technology
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/b18c3215adb9429ea61978187943b8e5
work_keys_str_mv	AT janarthananramadoss computervisionforhumancomputerinteractionusingnoninvasivetechnology AT jvenkatesh computervisionforhumancomputerinteractionusingnoninvasivetechnology AT shubhamjoshi computervisionforhumancomputerinteractionusingnoninvasivetechnology AT piyushkumarshukla computervisionforhumancomputerinteractionusingnoninvasivetechnology AT sajjadshaukatjamal computervisionforhumancomputerinteractionusingnoninvasivetechnology AT majidaltuwairiqi computervisionforhumancomputerinteractionusingnoninvasivetechnology AT basanttiwari computervisionforhumancomputerinteractionusingnoninvasivetechnology
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Computer Vision for Human-Computer Interaction Using Noninvasive Technology

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