Arif Rahmawan, Rahmi Hidayati, Kartika Sari

RTOS-BASED SYSTEM FOR TODDLER NUTRITIONAL STATUS DETECTION

Introduction

Rtos-based system for toddler nutritional status detection. Automatic RTOS-based system detects toddler nutritional status (24-60 months) using height/weight sensors and Z-score calculations. Accurate, efficient monitoring to assist healthcare workers.

Abstract

Determining the nutritional status of toddlers is essential for monitoring growth and preventing long-term health problems. Manual assessment requires significant time and is prone to human error; therefore, an automatic detection system based on height and weight parameters is needed. This study aims to develop a Real-Time Operating System (RTOS)–based system to detect the nutritional status of children aged 24–60 months, capable of managing task priorities, ensuring timely execution, and preventing race conditions using semaphores. The system employs an ultrasonic sensor to measure height, load cell sensors to measure body weight, and a web-based interface to input gender and age. Nutritional classification is determined through Z-score calculations using WHO reference data. Tests conducted on 200 children in various locations showed that the ultrasonic sensor achieved an average absolute error of 0.39 cm, a relative error of 0.409%, and an accuracy of 99.59%, while the load cell sensor achieved an average absolute error of 0.22 kg, a relative error of 1.587%, and an accuracy of 98.41%. The average execution times for the measurement and Z-score computation tasks were 4014.4 ms and 11.31 ms, respectively. The nutritional status classification results showed accuracy levels of 99.5% for Weight-for-Age (W/A), 99.5% for Height-for-Age (H/A), and 97.5% for Body Mass Index-for-Age (BMI/A) compared with manual assessments. The developed system demonstrated reliable performance in measurement and classification, with results consistent with conventional methods, indicating its potential as an efficient and accurate tool to assist healthcare workers in monitoring toddler nutrition status

Review

This paper presents a highly relevant and timely contribution to public health, addressing the critical need for efficient and accurate assessment of toddler nutritional status. The authors propose an innovative Real-Time Operating System (RTOS)–based system designed to automate height and weight measurements and subsequently classify nutritional status for children aged 24–60 months. A key strength lies in the intelligent integration of an RTOS to manage task priorities, ensure timely execution, and prevent race conditions using semaphores, which is crucial for a reliable embedded health monitoring device. This approach effectively tackles the inherent challenges of manual assessment, such as human error and time consumption, offering a promising solution for proactive health monitoring. The methodological design of the system is robust, employing ultrasonic and load cell sensors for accurate height and weight measurements, respectively, complemented by a web-based interface for demographic data input. The use of Z-score calculations based on WHO reference data for nutritional classification ensures clinical validity and adherence to international standards. The empirical evaluation on 200 children across various locations yielded impressive results: sensor accuracies of 99.59% for height and 98.41% for weight, demonstrating high precision. Furthermore, the system exhibited excellent classification accuracy compared to manual assessments, achieving 99.5% for Weight-for-Age, 99.5% for Height-for-Age, and 97.5% for Body Mass Index-for-Age. These performance metrics underscore the system's reliability and consistency with conventional diagnostic methods. Overall, the developed RTOS-based system represents a significant step forward in automated nutritional assessment. Its demonstrated reliable performance in both measurement and classification, coupled with efficient task execution, positions it as a valuable tool for healthcare workers. While the study effectively validates the technical capabilities and accuracy of the prototype, future work could explore long-term real-world deployment challenges, user experience improvements for healthcare providers in diverse settings, and scalability for broader public health programs. This research offers a compelling technological solution with substantial potential to improve the monitoring of toddler growth, ultimately aiding in the early detection and prevention of long-term health problems.

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