Monitoring the growth and development of toddlers is essential for the early detection of potential developmental delays, allowing for timely interventions. Traditional methods of growth tracking, such as physical charts, are less efficient and prone to errors. This can lead to delayed detection of growth abnormalities that may impact a child’s future development.
Students from the Dian Nuswantoro University student branch collaborated with The Integrated Service Post (POSYANDU), a local healthcare center that monitors toddler development, to create a system that automates the collection and analysis of growth data to detect early signs of developmental delays.
The F-Scales system uses an IoT-enabled smart scale to automatically measure toddlers’ height and weight. Cloud computing is used to store and process the data, while data analysis tracks growth trends over time. An algorithm compares each toddler’s data with established growth standards to identify early signs of growth abnormalities.
This innovative solution aims to promote healthier child development in the local community by enabling faster, more accurate monitoring and prevention.
This project was made possible by $2,000 in funding from EPICS in IEEE.