Assistive System for Monitoring Malnutrition in Children: MalnoCare

Malnutrition remains a critical global health issue, specifically affecting children, leading to stunting, cognitive impairments, and even death. Traditional assessment methods like manual MUAC measurements and dietary logs are labor-intensive, subjective, and impractical in resource-limited settings. To address this, MalnoCare integrates AI and mobile technology to enable early, non-invasive malnutrition detection. Using computer vision (OpenCV, MediaPipe) and smartphone cameras, it automates anthropometric measurements like height and MUAC, eliminating costly equipment and human error. Additionally, its food nutrient scanner analyses meal images via nutrition APIs, providing instant feedback on calories, proteins, and vitamins empowering caregivers with data-driven dietary decisions. Designed for scalability, MalnoCare integrates seamlessly into anganwadis, schools, and health centres. Its modular design supports future expansions, such as multilingual options and electronic scale connectivity. By offering a low-cost, user-friendly solution, MalnoCare bridges gaps in malnutrition screening, enabling proactive interventions in underserved communities Ultimately, MalnoCare transforms child health outcomes by equipping caregivers and health workers with accessible, real-time tools to combat malnutrition as a public health crisis.