AI-Driven Safety and Health Monitoring Systems: A Review of Adaptive Learning, Multimodal Sensor Fusion, and Predictive Intelligence | IJCT Volume 13 – Issue 5 | IJCT-V13I5P67
International Journal of Computer Techniques
ISSN 2394-2231
Author
Vetrikani R , Muskaan , Mythri B K , Jeevika B S , Tejashwini R , Prajwal S Ramaga
Abstract
Research on safety and health monitoring includes wearable fall detection, human activity recognition, computer vision, connected sensors, and chronic-disease monitoring. These areas share an interest in identifying events that may require assistance, but results from an individual dataset or task do not demonstrate that a complete emergency-response system will work reliably. This paper describes LENORA, a proposed AI-assisted monitoring system covering child safety, women’s safety, elderly monitoring, and adult disease-care monitoring. Its intended workflow links category-specific inputs with event analysis, risk assessment, dashboard support, safe-word verification, and location-aware communication. The literature summaries supplied for this review highlight practical concerns such as class imbalance, privacy, energy use, and deployment constraints. Project notes estimate that the software foundation is approximately 80% complete; this is a progress estimate, not a model-performance result. The available material does not verify that all sensors, AI models, GPS, GSM/SIM800L communication, and emergency-response steps have been integrated and tested. For that reason, this paper reports no LENORA accuracy, precision, recall, F1-score, latency, or deployment results. Instead, it records the current evidence boundaries and outlines tests for classification quality, false alarms, response time, communication reliability, and end-to-end behavior.
Keywords
wearable sensing, fall detection, Internet of Things, edge AI, risk assessment, emergency alerting, health monitoring
Conclusion
This paper describes LENORA’s proposed design and documented implementation status alongside supplied research on wearable fall detection, activity recognition, IoT safety, computer vision, and chronic-disease monitoring. The proposed architecture brings together category-specific inputs, event analysis, risk assessment, dashboard support, user verification, and emergency communication. The available evidence supports discussion of the design and reported software foundation, but it does not establish trained-model performance or a fully integrated hardware alert workflow. The evaluation plan identifies the evidence needed to assess classification quality, false alarms, response timing, communication reliability, and end-to-end operation. Quantitative results have not been added because they must come from completed, reproducible experiments.
References
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How to Cite This Paper
Vetrikani R , Muskaan , Mythri B K , Jeevika B S , Tejashwini R , Prajwal S Ramaga (2026). AI-Driven Safety and Health Monitoring Systems: A Review of Adaptive Learning, Multimodal Sensor Fusion, and Predictive Intelligence. International Journal of Computer Techniques, 13(5). ISSN: 2394-2231.
AI-Driven Safety and Health Monitoring Systems A Review of Adaptive Learning, Multimodal Sensor Fusion, and Predictive IntelligenceDownload
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