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%.

Original languageEnglish
Title of host publicationProceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering
Subtitle of host publicationApplying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages618-623
Number of pages6
ISBN (Electronic)9798350399615
DOIs
Publication statusPublished - 2022
Event6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022 - Virtual, Online, Indonesia
Duration: 13 Dec 202214 Dec 2022

Publication series

NameProceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022

Conference

Conference6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022
Country/TerritoryIndonesia
CityVirtual, Online
Period13/12/2214/12/22

Keywords

  • ADAS
  • CNN
  • Disability
  • LiDAR

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