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Visual-based fingertip detection for hand rehabilitation

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

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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.

Item Type: Article (Journal)
Additional Information: 7258/61384
Uncontrolled Keywords: Bag of Words, Fingertip detection, K-mean clustering, SURF
Subjects: T Technology > T Technology (General)
T Technology > TJ Mechanical engineering and machinery > TJ210.2 Mechanical devices and figures. Automata. Ingenious mechanism. Robots (General)
Kulliyyahs/Centres/Divisions/Institutes: Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User: Dr Ali Sophian
Date Deposited: 02 May 2018 12:05
Last Modified: 02 May 2018 12:05
URI: http://irep.iium.edu.my/id/eprint/61384

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