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Abstract
The reliable measurement of the pulse rate using remote photoplethysmography (PPG) is very important for many medical applications. In this paper we present how deep neural networks (DNNs) models can be used in the problem of PPG signal classification and pulse rate estimation. In particular, we show that the DNN-based classification results correspond to parameters describing the PPG signals (e.g. peak energy in the frequency domain, SNR, etc.). The results show that it is possible to identify regions of a face, for which reliable PPG signals can be extracted. The accuracy obtained for the classification task and the mean absolute error achieved for the regression task proved the usefulness of the DNN models.
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Authors (5)
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Alicja Kwaśniewska mgr inż
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Maciej Szankin mgr inż.
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Keywords
Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language:
- English
- Publication year:
- 2019
- Bibliographic description:
- Rumiński J., Kwaśniewska A., Szankin M., Kocejko T., Mazur-Milecka M.: Evaluation of Facial Pulse Signals Using Deep Neural Net Models// / : , 2019,
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/embc.2019.8857839
- Verified by:
- Gdańsk University of Technology
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