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Stairs descent identification for smart wheelchair by using GLCM and learning vector quantization

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The smart wheelchair helps the activities of someone who has a physical disability. The smart wheelchair has several capabilities, one of these capabilities is detecting obstacles in the form of stairs descent. Where if they are not aware of the stairs descent, they can fall, it will be an effect injuring. Therefore this study aims to create a system that is able to detect stairs descent based on digital image and provide notifications. The system was built using the Gray Level Co-occurrence Matrix method as feature extraction and Learning Vector Quantization to classify the stairs descent based on the digital image. From the results of the tests that have been carried out using 200 training data and 40 test data obtained an accuracy rate of 92.5 The faster average computation time is 0.02779 (s) for detecting the stairs descent.

Original languageEnglish
Title of host publicationProceedings - 2019 12th International Conference on Ubi-Media Computing, Ubi-Media 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages64-68
Number of pages5
ISBN (Electronic)9781728128207
DOIs
Publication statusPublished - Aug 2019
Event12th International Conference on Ubi-Media Computing, Ubi-Media 2019 - Bali, Indonesia
Duration: 6 Aug 20199 Aug 2019

Publication series

NameProceedings - 2019 12th International Conference on Ubi-Media Computing, Ubi-Media 2019

Conference

Conference12th International Conference on Ubi-Media Computing, Ubi-Media 2019
Country/TerritoryIndonesia
CityBali
Period6/08/199/08/19

Keywords

  • Gray level co-occurance matrix
  • Learning vector quantization
  • Smart wheelchair

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