Affiliations: [a] Department of Robotics and Artificial Intelligence Engineering, Faculty of Engineering and Technology, Applied Science Private University, Amman, Jordan | [b] Lebanese American University, Lebanon
Correspondence:
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Corresponding author: Mohammad Khaleel Sallam Ma’aitah, Department of Robotics and Artificial Intelligence Engineering, Faculty of Engineering and Technology, Applied Science Private University, 11931, Amman, Jordan. E-mail: [email protected].
Abstract: BACKGROUND: Deep learning has demonstrated superior performance over traditional methods for the estimation of heart rates in controlled contexts. However, in less controlled scenarios this performance seems to vary based on the training dataset and the architecture of the deep learning models. OBJECTIVES: In this paper, we develop a deep learning-based model leveraging the power of 3D convolutional neural networks (3DCNN) to extract temporal and spatial features that lead to an accurate heart rates estimation from RGB no pre-defined region of interest (ROI) videos. METHODS: We propose a 3D DenseNet with a 3D temporal transition layer for the estimation of heart rates from a large-scale dataset of videos that appear more hospital-like and real-life than other existing facial video-based datasets. RESULTS: Experimentally, our model was trained and tested on this less controlled dataset and showed heart rate estimation performance with root mean square error (RMSE) of 8.68 BPM and mean absolute error (MAE) of 3.34 BPM. CONCLUSION: Moreover, we show that such a model can also achieve better results than the state-of-the-art models when tested on the VIPL-HR public dataset.
Keywords: Heart rate, deep learning, rPPG, 3D convolutional neural networks, no pre-defined region of interest (ROI)
