@inproceedings{49567582a65344f9ae0ebe79b5efbd4e,
title = "Electric Wheelchair with Avoiding Obstacle Feature using LiDAR and CNN Method",
abstract = "Wheelchairs are one of the important medical instruments for the mobility of patients with disabilities to carry out several activities. Wheelchair has been significantly developed from the structures to features such as electric motors, controllers, and location detection system to improve the quality of safety and comfort. However, aspects of the smart obstacle avoider are currently being developed. In this study, an Advanced Driver-Assistance System (ADAS) has been developed using 2D LiDAR as an area mapping sensor, and Convolution Neural Network (CNN) to control a wheelchair embedded in a Raspberry Pi single board computer. The experimental results show that the success rate in avoiding obstacles automatically is between 76.6% to 100%.",
keywords = "ADAS, CNN, Disability, LiDAR",
author = "Shiddiqi and Muhammad Rivai and Rudy Dikairono and Djoko Purwanto and Sheva Aulia",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022 ; Conference date: 13-12-2022 Through 14-12-2022",
year = "2022",
doi = "10.1109/ICITISEE57756.2022.10057839",
language = "English",
series = "Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "618--623",
booktitle = "Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering",
address = "United States",
}