Inicio  /  Applied Sciences  /  Vol: 9 Par: 20 (2019)  /  Artículo
ARTÍCULO
TITULO

Improving Accuracy of Contactless Respiratory Rate Estimation by Enhancing Thermal Sequences with Deep Neural Networks

Alicja Kwasniewska    
Jacek Ruminski and Maciej Szankin    

Resumen

The proposed Super Resolution Deep Neural Network allows for improving accuracy of Respiratory Rate (RR) estimation from extremely low resolution thermal sequences, i.e., 40 × 30 pixels. To the best of our knowledge deep learning hasn?t been used for telemedicine use cases aimed at vital signs monitoring before. Thus, there are many potential applications where it can be useful, i.e., remote diagnostics using smart home platforms, long-distance vital signs monitoring in difficult to reach areas using cameras mounted on drones, monitoring of driver?s and passengers? state of health in self-driving vehicles, emotions recognition from vital signs, or detecting unusual behaviors e.g., abnormal respiratory rate patterns at security checkpoints.

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