首页|Reports from Brawijaya University Add New Study Findings to Research in Intellig ent Systems (Indoor staircase detection for supporting security systems in auton omous smart wheelchairs based on deep analysis of the Co-occurrence Matrix and . ..)
Reports from Brawijaya University Add New Study Findings to Research in Intellig ent Systems (Indoor staircase detection for supporting security systems in auton omous smart wheelchairs based on deep analysis of the Co-occurrence Matrix and . ..)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on intelligent systems h ave been presented. According to news reporting out of Malang, Indonesia, by New sRx editors, research stated, “Detecting descending stairs and floors is a cruci al aspect of implementing autonomous systems in smart wheelchairs.” The news correspondents obtained a quote from the research from Brawijaya Univer sity: “When the obstacle detection system used in wheelchairs fails to accuratel y identify descending stairs, it can lead to severe consequences for users, incl uding injuries or, in the worst-case scenario, fatal accidents. Therefore, there is a pressing need for an algorithm that not only exhibits high accuracy in det ecting obstacles on descending stairs but also operates with minimal computation al delay to ensure an immediate response in wheelchair braking. In this research , We utilize the GLCM technique to extract texture characteristics. Out of these methods, the Decision Tree exhibits the highest accuracy, reaching 94% , with a remarkably fast computational time of 0.01299 s. These promising result s were achieved by utilizing the GLCM method with a distance of 2 and an angle o f 45°. The accuracy obtained has increased by 2.5% compared to the previous research.”
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