<![CDATA[This study aims to develop an automated anthropometric system based on machine vision, integrated into a medical cyber-physical system (MCPS), to measure human head circumference. Head circumference is a critical parameter in growth monitoring, particularly for detecting abnormalities such as microcephaly and macrocephaly, which can affect cognitive development and overall health. To address this challenge, the study proposed an anthropometric system that enabled automated, accurate, and contactless measurements, accessible in real-time by healthcare professionals. The system was designed using a machine vision approach, incorporating object detection technology and elliptical model-based perimeter estimation to determine head circumference noninvasively. A 1,920 × 1,080-pixel (1080p) camera operating at 30 fps with a 60° field of view was mounted on a three-axis motion mechanism driven by stepper motors to automatically capture frontal and side views of the head. The measurement process began with head detection and bounding box adjustment to obtain head width parameters. Euclidean distance was used for measurement, followed by elliptical geometry modeling to estimate head circumference. Experimental results showed the lowest error rate of 2.29% at a distance of 50 cm under 300 lux lighting conditions. Performance evaluation using a confusion matrix yielded an accuracy of 92.8%, precision of 100%, recall of 97.5%, and F score of 98.7%. The proposed system provides an effective solution for healthcare professionals to perform growth screening quickly, accurately, and safely. It also supports remote healthcare services, particularly in areas with limited access to medical facilities.]]>
Copyrights © 2025
