Firefly algorithm and DNN for improved contactless heart rate measurement from videos
View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel contactless heart rate monitoring system using facial video analysis and a Deep Neural Network with Firefly optimization. This non-invasive method achieves high accuracy for heart rate classification in healthcare and fitness applications.
Area Of Science
- Biomedical Engineering
- Computer Science
- Artificial Intelligence
Background
- Contactless heart rate monitoring is crucial for non-invasive health assessment.
- Existing methods for facial-based heart rate detection face challenges in accuracy and feature selection.
- Deep learning approaches offer potential for improved physiological signal analysis.
Purpose Of The Study
- To develop a novel, accurate, and non-invasive system for heart rate measurement using facial video analysis.
- To enhance feature selection and classification accuracy for heart rate detection.
- To integrate Firefly optimization with a Deep Neural Network (DNN) for superior performance.
Main Methods
- Utilized facial video analysis for contactless heart rate measurement.
- Implemented a Deep Neural Network (DNN) architecture.
- Employed Firefly optimization for feature selection and model optimization.
- Evaluated performance using precision, recall, F-measure, and accuracy metrics.
Main Results
- Achieved a precision score of 90.22% and a recall score of 94.46%.
- Outperformed existing state-of-the-art methods by up to 2.1% in precision, 1.5% in recall, and 2.2% in F-measure.
- Demonstrated superior performance in classifying clinically meaningful heart rate categories.
Conclusions
- The proposed Firefly optimization-integrated DNN offers a dependable and accurate technique for non-contact heart rate measurement.
- This advancement holds significant promise for applications in healthcare, fitness tracking, and stress management.
- The method provides a clear advantage for non-contact heart rate classification compared to recent methodologies.

