Skeleton Joints Moment (SJM): A Hand Gesture Dimensionality Reduction for Central Nervous System Interaction
Recent breakthroughs with numerous visual experiences using mobile devices encourage the research of human-computer interaction (HCI) involving hand gesture recognition for Holograms, Virtual Reality, and Augmented Reality. The rise of these technologies allows educators in medical segments to apply...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9591567/ |
| _version_ | 1818931217336107008 |
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| author | Zainal Abdul Kahar Puteri Suhaiza Sulaiman Fatimah Khalid Azreen Azman |
| author_facet | Zainal Abdul Kahar Puteri Suhaiza Sulaiman Fatimah Khalid Azreen Azman |
| author_sort | Zainal Abdul Kahar |
| collection | DOAJ |
| description | Recent breakthroughs with numerous visual experiences using mobile devices encourage the research of human-computer interaction (HCI) involving hand gesture recognition for Holograms, Virtual Reality, and Augmented Reality. The rise of these technologies allows educators in medical segments to apply new pedagogy by interacting with virtual content in a coherent learning environment. This paper proposed the Central Nervous System (CNS) interaction using the Skeleton Joints Moment (SJM) approach for dimension reduction with k Nearest Neighbour (k-NN) for hand gesture classification. Over the past few decades, researchers have proposed various techniques in dimension reduction. One of the methods is principal component analysis (PCA). Experimental results indicated that the SJM technique has similar accuracy to PCA, where both methods showed 96% of prediction using hand skeleton joints data. In addition, PCA has a higher uncertainty of mean error 0.75 compared to SJM at only 0.01. Furthermore, PCA has the worst complexity of <inline-formula> <tex-math notation="LaTeX">$O(min(p^{3},n^{3}))$ </tex-math></inline-formula> where SJM <inline-formula> <tex-math notation="LaTeX">$O(n/d)$ </tex-math></inline-formula>. Evaluation results using the T-Test showed a significant difference between SJM and PCA where <inline-formula> <tex-math notation="LaTeX">$p < 0.05$ </tex-math></inline-formula>. Thus, there is evidence to reject the null hypothesis. |
| first_indexed | 2024-12-20T04:13:05Z |
| format | Article |
| id | doaj.art-4e266d5f8ae74008b6476774fe2c96ec |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T04:13:05Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4e266d5f8ae74008b6476774fe2c96ec2022-12-21T19:53:51ZengIEEEIEEE Access2169-35362021-01-01914664014665210.1109/ACCESS.2021.31235709591567Skeleton Joints Moment (SJM): A Hand Gesture Dimensionality Reduction for Central Nervous System InteractionZainal Abdul Kahar0https://orcid.org/0000-0002-5291-4395Puteri Suhaiza Sulaiman1https://orcid.org/0000-0002-8350-556XFatimah Khalid2https://orcid.org/0000-0002-5791-065XAzreen Azman3https://orcid.org/0000-0002-4118-4809Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Kembangan, MalaysiaFaculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Kembangan, MalaysiaFaculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Kembangan, MalaysiaFaculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Kembangan, MalaysiaRecent breakthroughs with numerous visual experiences using mobile devices encourage the research of human-computer interaction (HCI) involving hand gesture recognition for Holograms, Virtual Reality, and Augmented Reality. The rise of these technologies allows educators in medical segments to apply new pedagogy by interacting with virtual content in a coherent learning environment. This paper proposed the Central Nervous System (CNS) interaction using the Skeleton Joints Moment (SJM) approach for dimension reduction with k Nearest Neighbour (k-NN) for hand gesture classification. Over the past few decades, researchers have proposed various techniques in dimension reduction. One of the methods is principal component analysis (PCA). Experimental results indicated that the SJM technique has similar accuracy to PCA, where both methods showed 96% of prediction using hand skeleton joints data. In addition, PCA has a higher uncertainty of mean error 0.75 compared to SJM at only 0.01. Furthermore, PCA has the worst complexity of <inline-formula> <tex-math notation="LaTeX">$O(min(p^{3},n^{3}))$ </tex-math></inline-formula> where SJM <inline-formula> <tex-math notation="LaTeX">$O(n/d)$ </tex-math></inline-formula>. Evaluation results using the T-Test showed a significant difference between SJM and PCA where <inline-formula> <tex-math notation="LaTeX">$p < 0.05$ </tex-math></inline-formula>. Thus, there is evidence to reject the null hypothesis.https://ieeexplore.ieee.org/document/9591567/Hand gesture recognitiondimensionality reductionmachine learninghologram |
| spellingShingle | Zainal Abdul Kahar Puteri Suhaiza Sulaiman Fatimah Khalid Azreen Azman Skeleton Joints Moment (SJM): A Hand Gesture Dimensionality Reduction for Central Nervous System Interaction IEEE Access Hand gesture recognition dimensionality reduction machine learning hologram |
| title | Skeleton Joints Moment (SJM): A Hand Gesture Dimensionality Reduction for Central Nervous System Interaction |
| title_full | Skeleton Joints Moment (SJM): A Hand Gesture Dimensionality Reduction for Central Nervous System Interaction |
| title_fullStr | Skeleton Joints Moment (SJM): A Hand Gesture Dimensionality Reduction for Central Nervous System Interaction |
| title_full_unstemmed | Skeleton Joints Moment (SJM): A Hand Gesture Dimensionality Reduction for Central Nervous System Interaction |
| title_short | Skeleton Joints Moment (SJM): A Hand Gesture Dimensionality Reduction for Central Nervous System Interaction |
| title_sort | skeleton joints moment sjm a hand gesture dimensionality reduction for central nervous system interaction |
| topic | Hand gesture recognition dimensionality reduction machine learning hologram |
| url | https://ieeexplore.ieee.org/document/9591567/ |
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