Hybrid deep learning for IoT-based health monitoring with physiological event extraction

All these enhancements constitute a major step towards improving the IoT healthcare data processing chain, thereby providing a trusted and accurate system for real-time health monitoring and anomaly detection. In this regard, the research also paves the way for designing next-gen IoT healthcare analytics and their actual clinical applications.

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Publication Details

Journal
Digital Health
Published
2025-05-01
DOI
https://doi.org/10.1177/20552076251337848
Citations
4
Primary Topic
Non-Invasive Vital Sign Monitoring
Type
article
Field-Weighted Citation Impact
2.70
Controls
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article

Hybrid deep learning for IoT-based health monitoring with physiological event extraction

Kumar Debasis, Sivanagaraju Vallabhuni
4 citations
Digital Health
Non-Invasive Vital Sign Monitoring
2.70
article

Hybrid deep learning for IoT-based health monitoring with physiological event extraction

Kumar Debasis, Sivanagaraju Vallabhuni
article en
4 citations

Abstract

All these enhancements constitute a major step towards improving the IoT healthcare data processing chain, thereby providing a trusted and accurate system for real-time health monitoring and anomaly detection. In this regard, the research also paves the way for designing next-gen IoT healthcare analytics and their actual clinical applications.

Digital HealthVol. 11
SRM University (IN), VIT-AP University
Openalex Percentile: Top 10%
Non-Invasive Vital Sign Monitoring
2.70
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