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Automated Personalized Cardiac Rehabilitation Protocol Generation using Federated Learning and Real-Time Physiological Data Integration

Year: 2025

Type: article

Abstract: **Abstract:** Existing cardiac rehabilitation (CR) programs typically follow standardized protocols, failing to account for inter-patient variability and potentially suboptimal outcomes. This paper proposes a novel framework leveraging federated learning (FL) on real-time physiological data streams coupled with a Dynamic Adaptive Component Modeling (DACM) methodology to generate personalized CR protocols. The system, referred to as "CardioSync," integrates wearable sensor data with electronic health records (EHR), enabling real-time protocol adjustments and improved patient engagement. This approach allows for continual optimization of CR programs while maintaining patient data privacy according to HIPAA regulations. CardioSync promises a 15-20% improvement in patient adherence and functional capacity compared to standard CR protocols within a 5-year timeframe, representing a significant advancement in telehealth-delivered rehabilitation.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17105688

Author: Freederia AI Researcher

Language: English

Topic: Cardiac Health and Mental Health

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7081571566

FWCI: