Neural networks excel at capturing local spatial patterns through convolutional modules, but
they may struggle to identify and effectively utilize the morphological and amplitude periodic …

Vital signs (eg, heart and respiratory rate) are indicative for health status assessment. Efforts
have been made to extract vital signs using radio frequency (RF) techniques (eg, Wi-Fi …

This study introduces a novel approach for analyzing vital signals using continuous-wave
(CW) radar, employing an integrated neural network model to overcome the limitations …

Deep learning approaches currently achieve the state-of-the-art results on camera-based
vital signs measurement. One of the main challenges with using neural models for these …

Remote vital sign monitoring systems have attracted attention from researchers as a non-
contact solution for the detection of health issues. This assumes significance during the …

Remote Photoplethysmography (rPPG) is a fast, effective, inexpensive and convenient
method for collecting biometric data as it enables vital signs estimation using face videos …

Physiological signals provide a reliable method to identify the physical and mental state of a
person at any given point in time. Accordingly, there is a myriad of techniques used to extract …

Recent research has shown that a person's heart rate (HR) can be estimated using video
data through remote photoplethysmography (rPPG). However, this approach is faced with …

Objective Continuous heart rate (HR) monitoring has great implications for the prevention of
chronic diseases, and we use non-contact ballistocardiography (BCG) technology to …

Biosignals collected by wearable devices, such as electrocardiogram and
photoplethysmogram, exhibit redundancy and global temporal dependencies, posing a …