Empowering Anganwadi Workers with IoT and Forecasting Tools for Early Detection of Child Malnutrition in Tribal Regions of Chhattisgarh, India
DOI:
https://doi.org/10.48047/f65der29Keywords:
Severe Acute Malnutrition, Internet of Things (IoT), LoRa Communication, Anthropometric Measurement, Time-Series Forecasting, Edge Computing, Remote Health Mon- itoringAbstract
Severe Acute Malnutrition (SAM) continues to be a major public health challenge, especially in low-resource and tribal regions of India where infrastructure, skilled manpower, and real-time health monitoring are often lacking. Early identi- fication and timely intervention are critical in preventing serious health consequences and death among malnourished children. However, in many areas, traditional methods still depend on manual data entry and delayed reporting, which often result in late responses. To address these challenges, this paper proposes a low-cost, IoT-enabled framework that supports realtime remote monitoring and prediction of child malnutrition using minimal human intervention. The system uses automated weight and height sensors with facial recognition to correctly link measure- ments to individual children, even in rural Anganwadi centres. Data is transmitted using long-range (LoRa) wireless communi- cation, allowing uninterrupted operation in areas with poor or no mobile connectivity. A key feature of this framework is its ability to forecast nutritional trends. By analysing past growth data, the system predicts how a child’s nutritional condition may change in the coming months using WHO z-score indicators like Weight- for-Height (WHZ), Weight-for-Age (WFA), and Height-for-Age (HFA). Machine learning models such as ARIMA, SARIMA, and LSTM are used to forecast whether a child’s health is likely to improve or worsen. These insights empower Anganwadi workers to take preventive steps and provide timely counselling to caregivers, supported by clear evidence from the system. During field-testing in four tribal centres of Chhattisgarh, the framework made it easier for workers to collect more accurate data on time, helping them act faster and more confidently. This combined approach of using smart tools, local knowledge, and forecasting can improve early action and make child nutrition programs more effective in tribal and remote areas.
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