Remote Heart Rate Estimation Based on Transformer with Multi-Skip Connection Decoder: Method and Evaluation in the Wild.

Heart rate is an essential vital sign to evaluate human health. Remote heart monitoring using cheaply available devices has become a necessity in the twenty-first century to prevent any unfortunate situation caused by the hectic pace of life. In this paper, we propose a new method based on the transformer architecture with a multi-skip connection biLSTM decoder to estimate heart rate remotely from videos.

Iron Oxide Nanoparticle-Assisted Delamination of TiCT MXenes: A New Approach to Produce Magnetic MXene-Based Composites.

TiCT MXene is one of the most comprehensively studied 2D materials in terms of its adsorptive, transport, and catalytic properties, cytotoxic performance, etc. Still, conventional MXene synthesis approaches provide low single-flake MXene yield and frequently uncontrollable properties, demanding further post-processing. The MXene family also lacks magnetism, which is helpful for producing effective nanoadsorbents as their magnetic decantation is the cheapest and most convenient way to remove the spent adsorbent from water.

A Machine Learning-Based Correlation Analysis between Driver Behaviour and Vital Signs: Approach and Case Study.

Driving behaviour analysis has drawn much attention in recent years due to the dramatic increase in the number of traffic accidents and casualties, and based on many studies, there is a relationship between the driving environment or behaviour and the driver's state. To the best of our knowledge, these studies mostly investigate relationships between one vital sign and the driving circumstances either inside or outside the cabin. Hence, our paper provides an analysis of the correlation between the driver state (vital signs, eye state, and head pose) and both the vehicle maneuver actions (caused by the driver) and external events (carried out by other vehicles or pedestrians), including the proximity to other vehicles.

Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation.

One of the most effective vital signs of health conditions is blood pressure. It has such an impact that changes your state from completely relaxed to extremely unpleasant, which makes the task of blood pressure monitoring a main procedure that almost everyone undergoes whenever there is something wrong or suspicious with his/her health condition. The most popular and accurate ways to measure blood pressure are cuff-based, inconvenient, and pricey, but on the bright side, many experimental studies prove that changes in the color intensities of the RGB channels represent variation in the blood that flows beneath the skin, which is strongly related to blood pressure; hence, we present a novel approach to blood pressure estimation based on the analysis of human face video using hybrid deep learning models.