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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 1, 2023.
Abstract: This paper presents the study to differentiate between normal and anomaly conditions detected by humanoid robots using comparative statistics. The study has been conducted in robotic software as a platform to examine the scenario and evaluate between the anomalies and normal behaviour in different conditions. This study employed a machine vision technique to run an image segmentation process and carry out semi-supervised object training within a controlled environment. The robot is trained by differentiating the measurement size of the target object, its location, and the object’s visibility within three different frames. The effect is measured by extracting the positive predictive value (PPV) value, mean and standard deviation value from the captured image using statistical techniques in machine vision. The results showed that the mean value decreased by around 50% from the normal scenario when an anomaly occurred. Aside from that, the standard deviation values were more than twofold compared to the common scenario, especially after the object’s size grew. In contrast, the deviation value is remarkably small when the target is situated in the middle of adjacent frames, compared to the value when the entire shape is positioned in the frame. Simultaneously, the mean values from the processed image produced a minor difference.
Nuratiqa Natrah Mansor, Muhammad Herman Jamaluddin, Ahmad Zaki Shukor and Muhammad Sufyan Basri, “Comparative and Evaluation of Anomaly Recognition by Employing Statistic Techniques on Humanoid Robot” International Journal of Advanced Computer Science and Applications(IJACSA), 14(1), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140149
@article{Mansor2023,
title = {Comparative and Evaluation of Anomaly Recognition by Employing Statistic Techniques on Humanoid Robot},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140149},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140149},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {1},
author = {Nuratiqa Natrah Mansor and Muhammad Herman Jamaluddin and Ahmad Zaki Shukor and Muhammad Sufyan Basri}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.