Heart rate and respiratory rate prediction from noisy real-world smartphone based on Deep Learning methods

Word Count: 0 words + 7 table(s) 250 = 1750 words Submission Date: July 1, 2025 Farabi 2 ABSTRACT 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. Keywords: Vital signs, deep learning, regression, mobile phones, mHealth, photoplethysmogra-phy Farabi 3 INTRODUCTION The tracking of vital signs, such as heart rate (HR) and respiratory rate (RR), has become increasingly prevalent in daily life, serving as a general measure of human health and for quantifying symptoms in specific conditions such as atrial fibrillation ( 9), panic attacks ( 13), chronic obstructive pulmonary disease, asthma ( 4), and post-operative recovery ( 29). Fortunately, with the widespread availability of mobile phones, many individuals now have access to technology for taking these measurements without needing expensive and inconvenient companion devices, thus enabling measurements in resource-limited environments. In particular, the use of mobile phone videos for measuring vital signs has emerged as a promising option due to the near-ubiquity of smartphones, making it a convenient option for the elderly population.