Enhanced CNN–TCN–GTN–SE for Real-Time Heart Rate Classification in Personalized Cardiac Rehabilitation using IoT-driven Smart Wearable Device

Accurate heart rate monitoring is vital in assessing cardiovascular responses during rehabilitation, particularly for individuals with ischemic heart disease. However, conventional wearable systems often overlook inter-individual variations in heart rate dynamics and personalized cardiac performance, limiting their clinical value. This study presents an enhanced deep learning model that enables real-time classification using an IoT-driven smart wearable device and photoplethysmography (PPG) sensor. The proposed architecture integrates Convolutional Neural Networks (CNN), Temporal Convolutional Networks (TCN), Gated Transformer Networks (GTN), and Squeeze-and-Excitation (SE) blocks to extract both localized and long-range features from heart rate time-series data with [ADAM optimizer]. A total of 1,827 clinically labeled heart rate samples were collected from 38 patients undergoing a structured 480-second rehabilitation protocol. The model was trained and evaluated using 10-fold cross-validation and an independent test set, achieving an accuracy of 98.22% and a specificity of 98.53%. Ablation studies confirmed the contribution of each architectural component. This work introduces a new AI-powered solution, clinically scalable and one of the first models validated on real-world PPG data collected during structured and personalized cardiovascular rehabilitation. This work bridges wearable AI and clinical rehabilitation, offering substantial potential for real-time personalized cardiovascular monitoring and decision support in home-based settings.