Algoritmo de luciérnaga y DNN para mejorar la medición del ritmo cardíaco sin contacto a partir de vídeos

This study significantly improves heart rate analysis by enabling contactless heart measurement through facial video analysis. The system improves feature selection and classification accuracy by utilising a Deep Neural Network architecture with Firefly optimisation, offering a dependable technique for heart rate measurement from facial films. This advancement holds promise for non-invasive and convenient heart rate monitoring in various applications, including healthcare, fitness tracking, and stress management. This study proposes a novel approach integrating Firefly optimization with a Deep Neural Network (DNN) architecture for feature selection and classification tasks. Through comprehensive evaluation against state-of-the-art methodologies, the proposed algorithm demonstrates superior performance across multiple metrics including precision, recall, F-measure, and accuracy. Notably, achieving a precision score of 90.22% and a recall score of 94.46%, the algorithm outperforms existing methodologies, highlighting its efficacy in accurately identifying and classifying instances within the dataset. When compared with recent methods (Li et al., 2023; Kaur, 2022; Su et al., 2023), our method produced up to 2.1% greater precision, 1.5% greater recall, and 2.2% greater F-measure, which indicates a clear advantage for non-contact heart rate classification. Thus, the work grouped the heart rate values into relevant clinically meaningful categories and the performance of classification was measured using classification metrics.