Visual-based fingertip detection for hand rehabilitation
This paper presents a visual detection of fingertips by using a classification technique based on the bag-of-words method. In this work, the fingertips are specifically of people who are holding a therapy ball, as it is intended to be used in a hand rehabilitation project. Speeded Up Robust Features...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
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Institute of Advanced Engineering and Science (IAES)
2018
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| Online Access: | http://irep.iium.edu.my/61384/ http://irep.iium.edu.my/61384/11/61384-Visual-based%20fingertip%20detection%20for%20hand%20rehabilitation-SCOPUS.pdf http://irep.iium.edu.my/61384/17/61384_Visual-based%20fingertip%20detection%20for%20hand%20rehabilitation.pdf |
| _version_ | 1848785663429705728 |
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| author | Awang Za’aba, Dayang Qurratu’aini Sophian, Ali Sediono, Wahju Md. Yusof, Hazlina Sudirman, Sud |
| author_facet | Awang Za’aba, Dayang Qurratu’aini Sophian, Ali Sediono, Wahju Md. Yusof, Hazlina Sudirman, Sud |
| author_sort | Awang Za’aba, Dayang Qurratu’aini |
| building | IIUM Repository |
| collection | Online Access |
| description | This paper presents a visual detection of fingertips by using a classification technique based on the bag-of-words method. In this work, the fingertips are specifically of people who are holding a therapy ball, as it is intended to be used in a hand rehabilitation project. Speeded Up Robust Features (SURF) descriptors are used to generate feature vectors and then the bag-of-feature
model is constructed by K-mean clustering which reduces the number of features. Finally, a Support Vector Machine (SVM) is trained to produce a classifier that distinguishes whether the feature vector belongs to a fingertip or not. A total of 4200 images, 2100 fingertip images and 2100 non-fingertip images, were used in the experiment. Our results show that the success rates for the fingertip detection are higher than 94% which demonstrates that the proposed method produces a promising result for fingertip detection for therapy-ball-holding hands. |
| first_indexed | 2025-11-14T16:56:43Z |
| format | Article |
| id | iium-61384 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T16:56:43Z |
| publishDate | 2018 |
| publisher | Institute of Advanced Engineering and Science (IAES) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-613842018-05-02T04:05:05Z http://irep.iium.edu.my/61384/ Visual-based fingertip detection for hand rehabilitation Awang Za’aba, Dayang Qurratu’aini Sophian, Ali Sediono, Wahju Md. Yusof, Hazlina Sudirman, Sud T Technology (General) TJ210.2 Mechanical devices and figures. Automata. Ingenious mechanism. Robots (General) This paper presents a visual detection of fingertips by using a classification technique based on the bag-of-words method. In this work, the fingertips are specifically of people who are holding a therapy ball, as it is intended to be used in a hand rehabilitation project. Speeded Up Robust Features (SURF) descriptors are used to generate feature vectors and then the bag-of-feature model is constructed by K-mean clustering which reduces the number of features. Finally, a Support Vector Machine (SVM) is trained to produce a classifier that distinguishes whether the feature vector belongs to a fingertip or not. A total of 4200 images, 2100 fingertip images and 2100 non-fingertip images, were used in the experiment. Our results show that the success rates for the fingertip detection are higher than 94% which demonstrates that the proposed method produces a promising result for fingertip detection for therapy-ball-holding hands. Institute of Advanced Engineering and Science (IAES) 2018-02-01 Article PeerReviewed application/pdf en http://irep.iium.edu.my/61384/11/61384-Visual-based%20fingertip%20detection%20for%20hand%20rehabilitation-SCOPUS.pdf application/pdf en http://irep.iium.edu.my/61384/17/61384_Visual-based%20fingertip%20detection%20for%20hand%20rehabilitation.pdf Awang Za’aba, Dayang Qurratu’aini and Sophian, Ali and Sediono, Wahju and Md. Yusof, Hazlina and Sudirman, Sud (2018) Visual-based fingertip detection for hand rehabilitation. Indonesian Journal of Electrical Engineering and Computer Science, 9 (2). pp. 474-480. ISSN 2502-4752 E-ISSN 2502-4760 http://iaescore.com/journals/index.php/IJEECS/article/view/8760/7990 10.11591/ijeecs.v9.i2.pp474-480 |
| spellingShingle | T Technology (General) TJ210.2 Mechanical devices and figures. Automata. Ingenious mechanism. Robots (General) Awang Za’aba, Dayang Qurratu’aini Sophian, Ali Sediono, Wahju Md. Yusof, Hazlina Sudirman, Sud Visual-based fingertip detection for hand rehabilitation |
| title | Visual-based fingertip detection for hand rehabilitation |
| title_full | Visual-based fingertip detection for hand rehabilitation |
| title_fullStr | Visual-based fingertip detection for hand rehabilitation |
| title_full_unstemmed | Visual-based fingertip detection for hand rehabilitation |
| title_short | Visual-based fingertip detection for hand rehabilitation |
| title_sort | visual-based fingertip detection for hand rehabilitation |
| topic | T Technology (General) TJ210.2 Mechanical devices and figures. Automata. Ingenious mechanism. Robots (General) |
| url | http://irep.iium.edu.my/61384/ http://irep.iium.edu.my/61384/ http://irep.iium.edu.my/61384/ http://irep.iium.edu.my/61384/11/61384-Visual-based%20fingertip%20detection%20for%20hand%20rehabilitation-SCOPUS.pdf http://irep.iium.edu.my/61384/17/61384_Visual-based%20fingertip%20detection%20for%20hand%20rehabilitation.pdf |