Development of a Portable Ultrasonic Digital Anthropometry System with Automated CIAF Classification - SISWATI, TRI; Rialihanto, Muhammad Primiaji; Paramashanti, Bunga Astria; Ardiyanto, Farit; Attawet, Jutharat (Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics, 2026)
Child malnutrition, particularly stunting, remains a critical public health issue with long-term effects on growth and cognitive development. In many community health settings, conventional anthropometric measurement tools used in community health services often present challenges, including human measurement error, non-standard data recording, and a lack of real-time diagnostic output. This study aims to develop and validate a portable ultrasonic sensor-based digital anthropometric system capable of automatically detecting the Composite Index of Anthropometric Failure (CIAF) in real-time. The novelty of this research lies in integrating non-contact ultrasonic height measurement with automated CIAF classification based on WHO 2006 growth standards, cloud-connected data storage, and a user-friendly interface designed for community health workers. This Research and Development study involved system design, laboratory calibration, field validation, and user acceptability testing. A total of 80 toddlers and 30 users (midwives, nutritionists, and Posyandu cadres) participated across regions with low and high stunting prevalence. Measurement accuracy was compared to gold-standard anthropometry, while usability was assessed through a Likert-scale evaluation. Laboratory tests indicated measurement error ranging from 0.0 to 0.2 cm, indicating high sensor precision. Field tests showed a mean difference of ≤1 cm with no statistically significant difference (p>0.05) compared to standard measurement. User evaluation reported high satisfaction, particularly in ease of use (92%), accuracy (90%), and program support benefit (94%). The developed portable ultrasonic digital anthropometry system provides accurate, fast, and standardized CIAF-based malnutrition detection, supporting more efficient child growth monitoring programs. The tool demonstrates strong potential for integration into community-based nutrition surveillance and national health information systems.