Photoplethysmography (PPG) signals have become a key technology in many fields, such as medicine, well-being, or sports. Our work proposes set of pipelines to extract remote PPG (rPPG) from the face robustly, reliably, and configurable. We identify evaluate possible choices critical steps unsupervised rPPG methodologies. assess state-of-the-art processing pipeline six different datasets, incorporating important corrections methodology that ensure reproducible fair comparisons. In addition,...

Depression is a mental illness that may be harmful to an individual's health. The detection of health disorders in the early stages and precise diagnosis are critical avoid social, physiological, or psychological side effects. This work analyzes physiological signals observe if different depressive states have noticeable impact on blood volume pulse (BVP) heart rate variability (HRV) response. Although typically, HRV features calculated from biosignals obtained with contact-based sensors...

Objective: Respiratory infections are a leading cause of pediatric emergency visits globally, requiring timely and accurate assessment. This study evaluated the feasibility computer vision-based system to identify respiratory or distress estimate vital signs, including rate (RR), heart (HR), oxygen saturation (SpO2), in patients visiting an department. Methods: We conducted population-based case-control involving 100 children aged 0-5 years at Children's Emergency Department Oulu University...

Abstract Face alignment is a crucial component in most face analysis systems. It focuses on identifying the location of several keypoints human faces images or videos. Although methods and models are available to developers popular computer vision libraries, they still struggle with challenges such as insufficient illumination, extreme head poses, occlusions, especially when constrained by needs real-time applications. Throughout this article, we propose set training strategies...

Indoor human monitoring systems are integral in various applications. They leverage a wide range of sensors, including cameras, radio devices, and inertial measurement units, to collect extensive data from users the environment. These sensors contribute distinct modalities, encompassing video feeds received signal strength indicators channel state information WiFi three-axis acceleration accelerometers. In this context, we present comprehensive survey multimodal approaches applied indoor...

Meditation is a practice that aims at self-inducing state of calmed rest. In this work, we analyze physiological signals collected with wearable sensors to observe if meditation has noticeable effect on the human body response and inversely related stress can be detected using same biosignals similar features methods. Our work based extraction statistical extends models found in literature by extracting 30 additional heart rate variability. The results show wrist devices, periods...

Photoplethysmography (PPG) signals have become a key technology in many fields, such as medicine, well-being, or sports. Our work proposes set of pipelines to extract remote PPG (rPPG) from the face robustly, reliably, and configurable. We identify evaluate possible choices critical steps unsupervised rPPG methodologies. assess state-of-the-art processing pipeline six different datasets, incorporating important corrections methodology that ensure reproducible fair comparisons. In addition,...

Face detection and recognition are key components in multiple camera-based devices applications. Smart glasses a type of optical head mounted displays that integrate first-person cameras hands free with immediate access to processing power able analyze first person images real time operation. In this context, we have constructed an application prototype detects recognizes faces real-time, runs independently on the device. We provide description embedded implementation at system-level where...

Remote photoplethysmography (rPPG) offers a state-of-the-art, non-contact methodology for estimating human pulse by analyzing facial videos. Despite its potential, rPPG methods can be susceptible to various artifacts, such as noise, occlusions, and other obstructions caused sunglasses, masks, or even involuntary face touching. In this study, we apply image processing transformations intentionally degrade video quality, mimicking these challenging conditions, subsequently evaluate the...

Human Activity Recognition (HAR) from wearable sensor data identifies movements or activities in unconstrained environments. HAR is a challenging problem as it presents great variability across subjects. Obtaining large amounts of labelled not straightforward, since signals are easy to label upon simple human inspection. In our work, we propose the use neural networks for generation realistic and features using activity monocular videos. We show how these generated can be utilized, instead...

Radio frequency (RF) signals have facilitated the development of non-contact human monitoring tasks, such as vital signs measurement, activity recognition, and user identification. In some specific scenarios, an RF signal analysis framework may prioritize performance one task over that others. response to this requirement, we employ a multi-objective optimization approach inspired by biological principles select discriminative features enhance accuracy breathing patterns recognition while...

Exercise-induced fatigue resulting from physical activity can be an early indicator of overtraining, illness, or other health issues. In this paper, we present automated method for estimating exercise-induced levels through the use thermal imaging and facial analysis techniques utilizing deep learning models. Leveraging a novel dataset comprising over 400,000 images rested fatigued users, our results suggest that could predicted with only one static frame average error smaller than 15%. The...

The potential of generating authentic human facial images from remote photo-plethysmography (rPPG) signals is a compelling idea, with significant implications for biometric authentication and human-computer interaction. This study explores it by using large-scale dataset to train diffusion-based generative model, leveraging rPPG extracted videos. initial training phase yields promising results, the model demonstrating capacity synthesize likenesses that closely match corresponding subjects...

Video-based remote photoplethysmography (rPPG) has emerged as a promising technology for non-contact vital sign monitoring, especially under controlled conditions. However, the accurate measurement of signs in real-world scenarios faces several challenges, including artifacts induced by videocodecs, low-light noise, degradation, low dynamic range, occlusions, and hardware network constraints. In this article, we systematically investigate comprehensive these issues, measuring their...

In the domain of non-contact biometrics and human activity recognition, lack a versatile, multimodal dataset poses significant bottleneck. To address this, we introduce Oulu Multi Sensing (OMuSense-23) that includes biosignals obtained from mmWave radar, an RGB-D camera. The features data 50 individuals in three distinct -- standing, sitting, lying down each featuring four specific breathing pattern activities: regular breathing, reading, guided apnea, encompassing both typical situations...

Video-based remote photoplethysmography (rPPG) has emerged as a promising technology for non-contact vital sign monitoring, especially under controlled conditions. However, the accurate measurement of signs in real-world scenarios faces several challenges, including artifacts induced by videocodecs, low-light noise, degradation, low dynamic range, occlusions, and hardware network constraints. In this article, systematic comprehensive investigation these issues is conducted, measuring their...