Qurratu aini, D, Sophian, A, Sediono, W, Yusof, HM and Sudirman, S (2018) Visual-based fingertip detection for hand rehabilitation. Indonesian Journal of Electrical Engineering and Computer Science, 9 (2). ISSN 2502-4752
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Visual-based Fingertip Detection for Hand Rehabilitation r16.pdf - Accepted Version Download (1MB) | Preview |
Abstract
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. © 2018 Institute of Advanced Engineering and Science. All rights reserved.
Item Type: | Article |
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Subjects: | Q Science > QA Mathematics > QA76 Computer software R Medicine > RM Therapeutics. Pharmacology |
Divisions: | Computer Science & Mathematics |
Publisher: | Indonesian Journal of Electrical Engineering and Computer Science |
Date Deposited: | 20 Feb 2018 10:06 |
Last Modified: | 04 Sep 2021 10:45 |
DOI or ID number: | 10.11591/ijeecs.v9.i2.pp474-480 |
URI: | https://researchonline.ljmu.ac.uk/id/eprint/8059 |
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