International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 2 (March-April 2026) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
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Volume 8 Issue 2
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Edge-Based Intelligent Wearable IoT System for Real-Time Cardiac Anomaly Detection Using Machine

Author(s) Ms. Priyanka Sinha, Dr. Yogesh
Country India
Abstract Abstract
Wearable IoT devices have fundamentally changed how physiological data is gathered and interpreted in modern healthcare settings. Most current monitoring architectures depend on centralized cloud infrastructure to process sensor data, introducing transmission latency, bandwidth consumption, and exposure of sensitive patient information to external networks. This paper presents an edge-based intelligent framework that relocates the computational workload from the cloud to a local processing node-such as a smartphone or dedicated gateway-positioned closer to the patient. Sensor streams capturing heart rate, blood oxygen saturation (SpO2), and body temperature are processed and analysed in real time using three machine learning classifiers: Random Forest, Support Vector Machine (SVM), and Logistic Regression. Evaluation was conducted on the MIT-BIH Arrhythmia Database, a widely accepted PhysioNet benchmark containing over 100,000 annotated heartbeats from 48 recordings. Among the models tested, Random Forest achieved the strongest balance of classification accuracy (94%) and inference speed (120 ms), satisfying the sub-200 ms real-time threshold required for clinical alert systems. These findings support the argument that edge-centric architectures offer a practical and scalable path for continuous patient monitoring with meaningful improvements in response time and data privacy.
Keywords Edge computing, Internet of Things (IoT), wearable healthcare, machine learning, anomaly detection, real-time monitoring, arrhythmia classification, MIT-BIH
Field Computer Applications
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-04-14
DOI https://doi.org/10.36948/ijfmr.2026.v08i02.74633
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E-ISSN 2582-2160
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