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Heart rate monitoring is crucial in scientific and technical fields as it provides essential information about cardiovascular health, exercise performance, and stress levels, enabling early detection of and intervention for potential cardiac abnormalities or risks. Traditional methods for measuring heart rate often require direct contact with the body, which can be invasive and inconvenient. In this analysis, we have studied the remote photoplethysmography (rPPG) techniques for predicting heart wellness using different machine algorithms. To evaluate the effectiveness of different rPPG methods, we conducted a study with a diverse sample of 20 participants. We considered factors such as gender, skin texture (based on participants from India and Sierra Leone), and age group. By collecting data from various PPG and rPPG methods, we aimed to determine the most accurate technique for heart rate prediction. To accomplish this, we employed two machine learning algorithms: Lasso Regression and Random Forest Regression. These algorithms were trained on the collected heart rate data to predict and compare the performance of different rPPG methods. Our research findings indicate that both Random Forest Regression and Lasso Regression models exhibit promising results in predicting heart rate non-invasively and accurately. The Random Forest Regression model achieved an average mean square error of 3.193 and a coefficient of determination value of 0.885, while the Lasso Regression model achieved an average mean square error of 33.336 and a coefficient of determination, R2, value of 0.086. The relatively low Mean Squared Error (MSE) and high (R-squared) R2 values obtained from the Random Forest Regression model demonstrate its superior predictive performance compared to the Lasso Regression model. This suggests that the Random Forest algorithm is better suited for analyzing the collected heart rate prediction dataset using rPPG features. Our research findings underscore the potential of remote photoplethysmography (rPPG) and machine learning algorithms in predicting heart rate non-invasively. We have successfully analyzed the study method across different genders, regions, and skin colors. Moreover, our study emphasizes the significance of considering factors such as skin color pigments and their impact on the accuracy of heart rate predictions. By recognizing the influence of these factors, we can further refine and improve the performance of rPPG-based heart rate monitoring systems. |
