An Empirical Evaluation of Artificial Intelligence Algorithm for Hand Posture Classification

During the past decade, an intensive growth of Human�Computer Interaction (HCI) has been evolved. It includes, but is not limited to, Virtual Reality, Augmented Reality, Voice Control Systems, EEG-based systems, etc. Primarily, HCI is the hybridization of Information & Communication Technology...

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Main Authors: Hussain, A., Hussain, S.S., Uddin, M.M., Zubair, M., Kumar, P., Umair, M.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.28881 /
Published: Springer Science and Business Media Deutschland GmbH 2022
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114109620&doi=10.1007%2f978-3-030-76653-5_23&partnerID=40&md5=fef58d1e2e9b79f166b5b2187db96bdb
http://eprints.utp.edu.my/28881/
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spelling utp-eprints.288812022-03-17T02:21:51Z An Empirical Evaluation of Artificial Intelligence Algorithm for Hand Posture Classification Hussain, A. Hussain, S.S. Uddin, M.M. Zubair, M. Kumar, P. Umair, M. During the past decade, an intensive growth of Human�Computer Interaction (HCI) has been evolved. It includes, but is not limited to, Virtual Reality, Augmented Reality, Voice Control Systems, EEG-based systems, etc. Primarily, HCI is the hybridization of Information & Communication Technology and Bio-metric. Besides, the biometric inputs like Human Face, Voice, Physical actions, EEG signals, etc. are cascaded with the computing module for robust and intelligent decision making. The hand postures are one of the most common biometric for system automation & feedback. In this study, exhaustive empirical research of the machine learning algorithm for hand posture classification has been established. In this connection, a recent dataset, �Mocap Hand Postures Data Set,� has been opted to employ the different variants of the machine learning algorithm. To the best of the knowledge, the exhaustive comparative study on the said dataset is found to be deficient in the literature. Primarily the principal focus is to identify the best candidate for real-time hand posture classification. Moreover, the performance of each method has been rigorously measuring as a function of training accuracy, testing accuracy, prediction speed, and training time. Besides, the percentage recognition of each corresponding class is illustrated using the respective confusion matrix. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. Springer Science and Business Media Deutschland GmbH 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114109620&doi=10.1007%2f978-3-030-76653-5_23&partnerID=40&md5=fef58d1e2e9b79f166b5b2187db96bdb Hussain, A. and Hussain, S.S. and Uddin, M.M. and Zubair, M. and Kumar, P. and Umair, M. (2022) An Empirical Evaluation of Artificial Intelligence Algorithm for Hand Posture Classification. Intelligent Systems Reference Library, 210 . pp. 431-449. http://eprints.utp.edu.my/28881/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description During the past decade, an intensive growth of Human�Computer Interaction (HCI) has been evolved. It includes, but is not limited to, Virtual Reality, Augmented Reality, Voice Control Systems, EEG-based systems, etc. Primarily, HCI is the hybridization of Information & Communication Technology and Bio-metric. Besides, the biometric inputs like Human Face, Voice, Physical actions, EEG signals, etc. are cascaded with the computing module for robust and intelligent decision making. The hand postures are one of the most common biometric for system automation & feedback. In this study, exhaustive empirical research of the machine learning algorithm for hand posture classification has been established. In this connection, a recent dataset, �Mocap Hand Postures Data Set,� has been opted to employ the different variants of the machine learning algorithm. To the best of the knowledge, the exhaustive comparative study on the said dataset is found to be deficient in the literature. Primarily the principal focus is to identify the best candidate for real-time hand posture classification. Moreover, the performance of each method has been rigorously measuring as a function of training accuracy, testing accuracy, prediction speed, and training time. Besides, the percentage recognition of each corresponding class is illustrated using the respective confusion matrix. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
format Article
author Hussain, A.
Hussain, S.S.
Uddin, M.M.
Zubair, M.
Kumar, P.
Umair, M.
spellingShingle Hussain, A.
Hussain, S.S.
Uddin, M.M.
Zubair, M.
Kumar, P.
Umair, M.
An Empirical Evaluation of Artificial Intelligence Algorithm for Hand Posture Classification
author_sort Hussain, A.
title An Empirical Evaluation of Artificial Intelligence Algorithm for Hand Posture Classification
title_short An Empirical Evaluation of Artificial Intelligence Algorithm for Hand Posture Classification
title_full An Empirical Evaluation of Artificial Intelligence Algorithm for Hand Posture Classification
title_fullStr An Empirical Evaluation of Artificial Intelligence Algorithm for Hand Posture Classification
title_full_unstemmed An Empirical Evaluation of Artificial Intelligence Algorithm for Hand Posture Classification
title_sort empirical evaluation of artificial intelligence algorithm for hand posture classification
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114109620&doi=10.1007%2f978-3-030-76653-5_23&partnerID=40&md5=fef58d1e2e9b79f166b5b2187db96bdb
http://eprints.utp.edu.my/28881/
_version_ 1741197165196214272
score 11.62408

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