Comparative and evaluation of anomaly recognition by employing statistic techniques on humanoid robot

Mansor, Nuratiqa Natrah and Jamaluddin, Muhammad Herman and Shukor, Ahmad Zaki and Basri, Muhammad Sufyan (2023) Comparative and evaluation of anomaly recognition by employing statistic techniques on humanoid robot. International Journal of Advanced Computer Science and Applications, 14 (1). pp. 456-464. ISSN 2158-107X

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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.

Item Type:	Article
Uncontrolled Keywords:	Anomaly detection, Humanoid robot, Vision system, Statistical analysis, Robot recognition
Divisions:	Faculty of Electrical Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	30 Dec 2025 04:37
Last Modified:	30 Dec 2025 04:37
URI:	http://eprints.utem.edu.my/id/eprint/29339
Statistic Details:	View Download Statistic

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