ArticleScientific reports2026
HEAL: health-enhanced adaptive LoRaWAN for AI-based real-time soldier monitoring and status prediction.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Continuous and reliable monitoring of soldiers' health in remote and resource-constrained environments is essential for operational readiness and timely emergency interventions. Long Range Wide Area Network (LoRaWAN) provides an energy-efficient, long-range communication solution for wearable Internet of Medical Things (IoMT) devices, but dynamic factors such as packet loss, latency, and bandwidth limitations can compromise real-time health inference. This paper presents HEAL, Health-Enhanced Adaptive LoRaWAN, an AI-enabled IoMT framework designed to predict soldiers' health under dynamic physiological and communication conditions. "Adaptive" denotes AI-level robustness, where models sustain stable and accurate health predictions under varying communication constraints and transmission parameters. HEAL employs an end-to-end workflow in which a publicly available multivariate physiological time-series dataset is preprocessed, structured, and automatically labeled using transformer-based large language models. Multiple Deep Learning (DL) architectures are trained and compared to identify models suitable for real-time health status classification, with the best-performing model fine-tuned and deployed in a LoRaWAN simulation to evaluate AI inference under realistic communication constraints. Experimental results show that a BiLSTM model achieves up to 94% accuracy and 95% Macro F1-score on structured physiological data. Within the LoRaWAN simulation, the fine-tuned model maintains approximately 93% accuracy, while the network achieves a packet delivery ratio of up to 91% under favorable conditions (SF7-SF8, 200-400 m). HEAL provides a scalable and energy-efficient framework for systematically evaluating AI models alongside communication reliability metrics, demonstrating that accurate AI-driven health monitoring can be sustained despite dynamic LoRaWAN effects and bridging the gap between model development and realistic operational deployment.
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