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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Hurriyatul Fitriyah
Brawijaya University
Computer Science & IT Electrical & Electronics Engineering

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Fast Obstacle Distance Estimation using Laser Line Imaging Technique for Smart Wheelchair Fitri Utaminingrum; Hurriyatul Fitriyah; Randy Cahya Wihandika; M Ali Fauzi; Dahnial Syauqy; Rizal Maulana
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 4: August 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (497.611 KB) | DOI: 10.11591/ijece.v6i4.pp1602-1609

Abstract

This paper presents an approach of obstacle distance estimation for smart wheelchair. A smart wheelchair was equipped with a camera and a laser line. The camera was used to capture an image from the environment in order to sense the pathway condition. The laser line was used in combination with camera to recognize an obstacle in the pathway based on the shape of laser line image in certain angle. A blob method detection was then applied on the laser line image to separate and recognize the pattern of the detected obstacles. The laser line projector and camera which was mounted in fixed-certain position ensured a fixed relation between blobs-gap and obstacle-to-wheelchair distance. A simple linear regression from 16 obtained data was used to respresent this relation as the estimated obstacle distance. As a result, the average error between the estimation and the actual distance was 1.25 cm from 7 data testing experiments. Therefore, the experiment results show that the proposed method was able to estimate the distance between wheelchair and the obstacle.
Classification of lung condition for early diagnosis of pneumonia and tuberculosis based on embedded system Rizal Maulana; Alfatehan Arsya Baharin; Hurriyatul Fitriyah
Bulletin of Electrical Engineering and Informatics Vol 10, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i3.3033

Abstract

The lungs are the main organs in the respiratory system that have a function as a place for exchange of oxygen and carbon dioxide. Due to the importance of lung function, indications of lung disorders must be detected and diagnosed early. Research on the classification of lung conditions generally uses chest x-ray image data. Where a time-consuming procedure is needed to obtain the data. In this research, an embedded system to diagnose lung conditions was designed. The system was made to be easy to use independently and provides real-time examination results. This system uses parameters of body temperature, oxygen saturation, fingernail color and lung volume in classifying lung conditions. There are three conditions that can be classified by the system, that is healthy lungs, pneumonia and tuberculosis. The k-nearest neighbor method was used in the classification process in the designed system. The dataset used was 51 data obtained from the hospital. Each data already has a label in the form of lung condition based on the doctor’s diagnosis. The proposed system has an accuracy of 88.24% in classifying lung conditions.
Co-Authors Alfatehan Arsya Baharin Dahnial Syauqy M. Ali Fauzi Randy Cahya Wihandika Rizal Maulana Utaminingrum, Fitri