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...

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Main Authors:	Awang Za’aba, Dayang Qurratu’aini, Sophian, Ali, Sediono, Wahju, Md. Yusof, Hazlina, Sudirman, Sud
Format:	Article
Language:	English English
Published:	Institute of Advanced Engineering and Science (IAES) 2018
Subjects:	T Technology (General) TJ210.2 Mechanical devices and figures. Automata. Ingenious mechanism. Robots (General)
Online Access:	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

id	iium-61384
recordtype	eprints
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
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	T Technology (General) TJ210.2 Mechanical devices and figures. Automata. Ingenious mechanism. Robots (General)
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
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.
format	Article
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
title	Visual-based fingertip detection for hand rehabilitation
title_short	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_sort	visual-based fingertip detection for hand rehabilitation
publisher	Institute of Advanced Engineering and Science (IAES)
publishDate	2018
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
first_indexed	2023-09-18T21:27:04Z
last_indexed	2023-09-18T21:27:04Z
_version_	1777412262762381312

Visual-based fingertip detection for hand rehabilitation

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