Heart rate and respiratory rate prediction from noisy real-world smartphone based on Deep Learning methods
This work addresses the challenge of reliable vital sign monitoring in daily life for healthcare applications, representing a strong specific gain over traditional methods.
The study tackled the problem of accurately predicting heart rate and respiratory rate from noisy real-world smartphone videos, demonstrating a 68% reduction in heart rate error and a 75% reduction in respiratory rate error using a novel 3D deep CNN method.
Using mobile phone video of the fingertip as a data source for estimating vital signs such as heart rate (HR) and respiratory rate (RR) during daily life has long been suggested. While existing literature indicates that these estimates are accurate to within several beats or breaths per minute, the data used to draw these conclusions are typically collected in laboratory environments under careful experimental control, and yet the results are assumed to generalize to daily life. In an effort to test it, a team of researchers collected a large dataset of mobile phone video recordings made during daily life and annotated with ground truth HR and RR labels from N=111 participants. They found that traditional algorithm performance on the fingerprint videos is worse than previously reported (7 times and 13 times worse for RR and HR, respectively). Fortunately, recent advancements in deep learning, especially in convolutional neural networks (CNNs), offer a promising solution to improve this performance. This study proposes a new method for estimating HR and RR using a novel 3D deep CNN, demonstrating a reduced error in estimated HR by 68% and RR by 75%. These promising results suggest that regressor-based deep learning approaches should be used in estimating HR and RR.