Road Surface Detection for Autonomous Smart Wheelchair
4
Issued Date
2022-01-01
Resource Type
Scopus ID
2-s2.0-85141644144
Journal Title
WSCE 2022 - 2022 5th World Symposium on Communication Engineering
Start Page
69
End Page
73
Rights Holder(s)
SCOPUS
Bibliographic Citation
WSCE 2022 - 2022 5th World Symposium on Communication Engineering (2022) , 69-73
Suggested Citation
Utaminingrum F., Mayena S., Karim C., Wahyudi S., Huda F.A., Lin C.Y., Shih T.K., Thaipisutikul T. Road Surface Detection for Autonomous Smart Wheelchair. WSCE 2022 - 2022 5th World Symposium on Communication Engineering (2022) , 69-73. 73. doi:10.1109/WSCE56210.2022.9916050 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84345
Title
Road Surface Detection for Autonomous Smart Wheelchair
Author's Affiliation
Other Contributor(s)
Abstract
Today's wheelchair technology is evolving, with options ranging from manual wheelchairs, electric wheelchairs to the latest smart wheelchairs. However, wheelchairs on the market today still lack of the resources to facilitate advanced security for their users, particularly from the risk of injury or death caused by user neglect to differences in the rough and uneven texture surface structure and distinctions in road height levels when crossed by wheelchairs. Which would be at risk of accidents such as the existence of descending stairs. The victim may sustain physically visible physiological effects because of the accident, such as abrasions, bruises, tears, fractures, head injuries, and even death in fatal cases. The goal of this research is to enhance the security and safety of smart wheelchairs by developing autonomous controls with the extraction of gray level cooccurrence matrix (GLCM) texture features and the Naive Bayes Classification in anticipation of irregular road conditions and the existence of levels that are at risk of endangering wheelchair users based on camera input. According to the results of 100 experiments using 2-dimensional imagery data tested at d = 1, 2, 3, 4 and θ = 0°, 45°, 90°, 135° values, the resulting d = 1 and θ = 0° levels have the highest accuracy of 87% when classifying images of descending stairs and floors.