Ahmad Wali Satria Bahari Johan Satria, Fitri Utaminingrum Fitri, Timothy K. Shih Timothy
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. © 2019 IEEE.
Afiliasi Computer Vision, Research Group, Faculty of Computer Science, Brawijaya University, Malang, Indonesia; Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
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