Enhanced Deep Learning Model for Vital Signs Real-Time Monitoring

• Author(s): Joy Azanor; Maureen Ifeanyi Akazue; Okpomi Arerebo Profit
• Paper ID: 1711432
• Page: 1242-1252
• Published Date: 27-10-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 4 October-2025

Abstract

Internet of Things (IoT)- based remote health monitoring systems have an enormous potential to become an integral part of the future medical system. IoT based systems plays a life-saving role in treating or monitoring patients with critical health issues, and reduce pressure on the healthcare system. Any healthcare monitoring system must be free from erroneous data, which may arise because of instrument failure or communication errors. In this paper, Convolutional Neural, adeep-learning technique, was applied to detect the reliability and accuracy of data obtained by IoT-based remote health monitoring. This data is sent to the intermediate device and then to the cloud for erroneous data detection. In the first approach, an unsupervised classifier called Auto Encoder (AE) is used for labelling data by using the latent features. Then the labelled data from AE is used as ground truth for comparing the accuracy of deep learning models. In the second approach, the raw data is labelled based on the correlation between various features.

Keywords

Deep Learning; Health system, Vital Signs; Real-TimeMonitoring, Internet of Things

Citations

IRE Journals:
Joy Azanor, Maureen Ifeanyi Akazue, Okpomi Arerebo Profit "Enhanced Deep Learning Model for Vital Signs Real-Time Monitoring" Iconic Research And Engineering Journals Volume 9 Issue 4 2025 Page 1242-1252 https://doi.org/10.64388/IREV9I4-1711432-9943

IEEE:
Joy Azanor, Maureen Ifeanyi Akazue, Okpomi Arerebo Profit "Enhanced Deep Learning Model for Vital Signs Real-Time Monitoring" Iconic Research And Engineering Journals, 9(4) https://doi.org/10.64388/IREV9I4-1711432-9943