Real-Time Vital Parameter Analysis Using Deep Convolutional Neural Networks for Remote Healthcare

Mercy Chiamaka Umeh; Precious Chinelo Okoro

doi:https://doi.org/10.47001/JAIET/2026.307001

Authors

Mercy Chiamaka Umeh

Faculty of Computing Science and Engineering, Obafemi Awolowo University, Nigeria

Precious Chinelo Okoro

Department of Biomedical Engineering, Federal University of Technology Akure, Nigeria

Abstract

Continuous monitoring of vital physiological parameters plays a crucial role in the early detection of medical abnormalities, remote patient care, and timely clinical intervention. Conventional health monitoring systems primarily rely on threshold-based analysis and manual interpretation, which often lack the capability to identify complex physiological patterns and may generate delayed or inaccurate alerts. This paper presents a Real-Time Vital Parameter Analysis framework using Deep Convolutional Neural Networks (CNNs) for intelligent remote healthcare monitoring. The proposed system integrates wearable and Internet of Things (IoT)-enabled biomedical sensors to continuously acquire essential physiological parameters, including heart rate, blood pressure, body temperature, respiratory rate, blood oxygen saturation (SpO₂), and electrocardiogram (ECG) signals. The collected physiological data undergo preprocessing techniques such as noise filtering, normalization, segmentation, and feature enhancement before being processed by a deep CNN architecture. Unlike conventional machine learning approaches that require handcrafted feature extraction, the proposed CNN model automatically learns discriminative hierarchical features directly from raw physiological signals, thereby improving classification performance. The trained model accurately distinguishes between normal and abnormal health conditions while minimizing false alarms and enhancing diagnostic reliability. Experimental analysis demonstrates that the proposed framework achieves high classification accuracy, improved sensitivity, specificity, and reduced computational latency, making it suitable for real-time deployment on cloud-based and edge computing platforms. Furthermore, the system supports remote healthcare services by enabling continuous patient monitoring, early disease prediction, and automated clinical decision support, thereby reducing hospital visits and improving healthcare accessibility. The proposed deep learning-based healthcare framework offers a scalable, reliable, and intelligent solution for next-generation telemedicine and smart healthcare applications.

Keywords

Real-Time Healthcare Monitoring Deep Convolutional Neural Networks (CNN) Vital Parameter Analysis Remote Patient Monitoring Internet of Things (IoT) Physiological Signal Processing Biomedical Sensors Artificial Intelligence Telemedicine Smart Healthcare Deep Learning Clinical Decision Support.

Citation of this Article

Mercy Chiamaka Umeh, & Precious Chinelo Okoro. (2026). Real-Time Vital Parameter Analysis Using Deep Convolutional Neural Networks for Remote Healthcare. Journal of Artificial Intelligence and Emerging Technologies (JAIET). 3(7), 1-16. Article DOI: https://doi.org/10.47001/JAIET/2026.307001

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