Firefly algorithm and DNN for improved contactless heart rate measurement from videos

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.