A hybrid machine learning framework combining Multilayer Perceptron, Naïve Bayes, and K-Nearest Neighbors achieved 96.67% accuracy in predicting heart rate status during physical activity.
A hybrid machine learning ensemble model accurately classifies heart rate status during physical activity and is suitable for real-time deployment on embedded platforms.
Real-time accurate evaluation of heart rate status during physical activity is a critical requirement in physiological analysis, performance enhancement, and early warning systems for cardiovascular diseases. In this work, a lightweight hybrid machine learning model for classifying the status of heart rate during physical activities into normal and warning levels using a combination strategy of Multilayer Perceptron, Naïve Bayes, and K-Nearest Neighbors algorithms in a weighted voting–based ensemble model is presented. The model analyzes a set of physiological and environmental parameters such as temperature, humidity, speed, incline, and activity time. Before embarking on model development, a thorough statistical analysis of available data was performed, which involved descriptive statistics, density analysis, normality tests using Shapiro–Wilk tests, and both parametric and non-parametric hypothesis tests to establish the discriminative power of all input variables in spite of non-normality. Every individual base classifier model was tested for performance using a fixed split of 70/15/15 for training, validation, and testing, and their respective strengths were tapped using a weighted voting mechanism based on relief factor analysis. The simulation outcome shows that the proposed ensemble classifier performs better than standalone classifiers in achieving an accuracy of up to 96.67%, an F1-score of 96.66%, and an MCC of 0.9354, which is a manifestation of excellent classification balance and statistical significance. The proposed system was also tested on an embedded system developed using an Arduino platform for real-time processing, and it achieved an accuracy of 90.83% and an MCC of 0.8167. The performance degradation is due to sensor noises and limited numerical precision in an embedded system. The proposed framework contributes to improving real-time heart rate monitoring by addressing computational constraints and enabling efficient deployment on embedded platforms.
Emami et al. (2026) studied Heart rate status during physical activity (n=40). Hybrid machine learning framework (MLP, Naïve Bayes, KNN) vs. Standalone classifiers was evaluated on Classification accuracy for heart rate status. A hybrid machine learning framework combining Multilayer Perceptron, Naïve Bayes, and K-Nearest Neighbors achieved 96.67% accuracy in predicting heart rate status during physical activity.