Sistem Antropometri Lingkar Kepala Manusia berbasis Machine Vision

Susetyo Bagas Bhaskoro; Sandy Bhawana Mulia; Afiq Hasydhiqi

doi:10.22146/jnteti.v14i3.20175

Susetyo Bagas Bhaskoro Cyber Physical System Study Program, Department of Manufacturing Automation Engineering and Mechatronics, Politeknik Manufaktur Bandung, Bandung, Jawa Barat 40135, Indonesia
Sandy Bhawana Mulia Program Studi Teknologi Rekayasa Mekatronika, Department of Manufacturing Automation Engineering and Mechatronics, Politeknik Manufaktur Bandung, Jawa Barat 40135, Indonesia
Afiq Hasydhiqi Program Studi Teknologi Rekayasa Otomasi, Department of Manufacturing Automation Engineering and Mechatronics, Politeknik Manufaktur Bandung, Jawa Barat 40135, Indonesia

DOI: https://doi.org/10.22146/jnteti.v14i3.20175

Keywords: Anthropometric System, Head Circumference, Noncontact Measuring Device, Machine Vision, Elliptical Perimeter, Medical Cyber-Physical System

Abstract

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.

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A Machine Vision-Based Anthropometric System for Measuring Human Head Circumference

Abstract

References