Big Data Analytics for Remote Patient Monitoring in Cyber-Physical Healthcare Systems

Shashank Mallesh; Anitha Devi M. D.; Chandana Sreenivas; Mala K.

doi:10.48084/etasr.18957

Authors

Shashank Mallesh Department of Business Analytics & Data Science, Saint Louis University, St Louis, Missouri, USA
Anitha Devi M. D. Department of Electronics and Communication Engineering, Sri Siddhartha Institute of Technology, Sri Siddhartha Academy of Higher Education, Tumakuru, India
Chandana Sreenivas Supply Chain Management, Saint Louis University, St Louis, Missouri, USA
Mala K. Department of Information Science and Engineering, Channabasaveshwara Institute of Technology, Tumakuru, India | Visvesvaraya Technological University, Belagavi, India

Volume: 16 | Issue: 4 | Pages: 37571-37576 | August 2026 | https://doi.org/10.48084/etasr.18957

Received: 27 March 2026 | Revised: 1 May 2026 and 18 May 2026 | Accepted: 19 May 2026 | Online: 11 June 2026

Corresponding author: Shashank Mallesh

Abstract

The convergence of Big Data analytics with Cyber-Physical Systems (CPS) presents a transformative opportunity for Remote Patient Monitoring (RPM) in modern healthcare. Despite advances in the Internet of Medical Things (IoMT) and distributed computing, the existing RPM frameworks lack integrated support for real-time multi-modal physiological analysis at scale. This paper proposes a six-layer Big Data analytics framework for RPM in cyber-physical healthcare settings, integrating IoMT wearable sensing, edge computing preprocessing, Apache Kafka stream ingestion, Apache Spark distributed processing, and a hierarchical Long Short-Term Memory (LSTM) deep learning model for real-time anomaly detection. The key contributions of the current paper include: (i) a scalable six-layer CPS architecture for continuous physiological monitoring; (ii) a distributed Big Data pipeline achieving sub-2-second end-to-end alert latency, and (iii) an LSTM-based classifier supporting eight physiological condition classes with high accuracy. Experimental evaluation on a real-participant dataset demonstrates competitive performance, with low latency and strong clinician usability scores, indicating promising readiness for broader clinical evaluation and deployment.

Keywords:

Remote Patient Monitoring (RPM), big data analytics, cyber-physical healthcare systems, Long Short-Term Memory (LSTM), Apache Spark, Internet of Medical Things (IoMT), anomaly detection, edge computing, deep learning, healthcare informatics

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