Visual-Based Fingertip Detection for Hand Rehabilitation

Dayang Qurratu’aini (Department of Mechatronics Engineering, Kulliyyah of Engineering, International Islamic University Malaysia, Jalan Gombak, 53100 Kuala Lumpur, Malaysia)
Ali Sophian (International Islamic University Malaysia)
Wahju Sediono (Department of Mechatronics Engineering, Kulliyyah of Engineering, International Islamic University Malaysia)
Hazlina Md Yusof (Department of Mechatronics Engineering, Kulliyyah of Engineering, International Islamic University Malaysia)
Sud Sudirman (School of Computing and Mathematical Sciences, Liverpool John Moores University)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 9, No 2: February 2018

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 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.