Lightweight IoT-enabled Wearables for Early Disease Prediction in Resource-Constrained Environments
Keywords:
Edge computing, E-healthcare, IoT wearables, Lightweight machine learning, Resource-constrained environments, Early disease predictionAbstract
The proliferation of Internet of Things (IoT) technologies has enabled the development of wearable devices for continuous health monitoring and the early detection of diseases. However, deploying such systems in resource-constrained environments—such as rural areas with limited computational power, unreliable connectivity, and scarce medical infrastructure remains a significant challenge. This study proposes a lightweight IoT-enabled wearable framework that leverages energy-efficient sensors, edge computing, and optimized machine learning models to enable real-time health monitoring. The system prioritizes low-power consumption, cost-effectiveness, and robustness in adverse conditions. Simulation results and comparative analysis with existing systems demonstrate that the proposed architecture significantly improves predictive accuracy and device longevity while maintaining affordability. This approach has the potential to bridge healthcare access gaps and enable timely disease intervention in underserved communities.
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