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 |
| Published: |
Institute of Advanced Engineering and Science (IAES)
2018
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| Subjects: | |
| 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 |
| Summary: | 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. |
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