Iskanje po repozitoriju

A+ | A- | | SLO | ENG

Povzetek: This paper introduces a uniform evaluation framework for assessing the effectiveness of past non-pharmaceutical interventions (NPIs) in managing infectious diseases, taking into account the cultural and social differences between countries. The framework enables quantifying and finding the optimal balance of both the health and socioeconomic impacts of NPIs. The aim is to assist policymakers in understanding which NPIs lead to the optimal balance by highlighting unnecessary costs - the costs that could be avoided while maintaining the same infection rates. To assess the extent of unnecessary socioeconomic consequences experienced by a country during a past epidemic of infectious diseases, we use the following approach. First, we develop a model that predicts the number of infections from NPIs in a country. Second, we estimate the socioeconomic costs (SEC) of the NPIs universally for countries included in the study. Third, we develop a model that prescribes the NPI plans with the optimal trade-off between the number of infections and the SEC. Fourth, we create a model that specifically adjusts each country’s SEC. Finally, we provide additional analysis to increase comprehension of the effects of NPIs. Demonstrated through an analysis of COVID-19 pandemic responses in 17 countries, the study offers a systematic presentation of the framework and a concrete examination of the integrated effects of NPIs. It provides insights into interventions’ direct and indirect consequences, offering guidance for future epidemic responses. The framework enables a systematic understanding of the effects of the NPIs applied, acknowledging the national diversity in health measure acceptance and implementation. This allows for fair analysis across countries, identifying and displaying the economic, social, and health-related costs of suboptimal NPI strategies, i.e., unnecessary costs. The framework is applicable for any infectious disease, NPIs, or country, assuming the medical interventions are similar, e.g., timing and amount of vaccination
Ključne besede: Bombus dahlbomii, Patagonian bumblebee, invasive species, population dynamics, socioeconomic consequences, infectious diseases
Objavljeno v DiRROS: 14.11.2025; Ogledov: 148; Prenosov: 58
Celotno besedilo (10,29 MB)
Gradivo ima več datotek! Več...

Povzetek: Work-related well-being is an important research topic, as it is linked to various aspects of individuals’ lives, including job performance. To measure it effectively, unobtrusive sensors are desirable to minimize the burden on employees. Because there is a lack of consensus on the definitions of well-being in the psychological literature in terms of its dimensions, our work begins by proposing a conceptualization of well-being based on the refined definition of health provided by the World Health Organization. We focus on reviewing the existing literature on the unobtrusive measurement of well-being. In our literature review, we focus on affect, engagement, fatigue, stress, sleep deprivation, physical comfort, and social interactions. Our initial search resulted in a total of 644 studies, from which we then reviewed 35, revealing a variety of behavioral markers such as facial expressions, posture, eye movements, and speech. The most commonly used sensory devices were red, green, and blue (RGB) cameras, followed by microphones and smartphones. The methods capture a variety of behavioral markers, the most common being body movement, facial expressions, and posture. Our work serves as an investigation into various unobtrusive measuring methods applicable to the workplace context, aiming to foster a more employee-centric approach to the measurement of well-being and to emphasize its affective component.
Ključne besede: affective computing, ambient intelligence, work-related well-being, behavioral markers
Objavljeno v DiRROS: 08.07.2025; Ogledov: 380; Prenosov: 227
Celotno besedilo (565,62 KB)
Gradivo ima več datotek! Več...

Povzetek: Virtual reality (VR) technology is often referred to as the ‘ultimate empathy machine’ due to its capability to immerse users in alternate perspectives and environments beyond their immediate physical reality. In this study, participants will be immersed in 3-dimensional 360˚ VR videos where actors express different emotions (sadness, happiness, anger, and anxiousness). The primary objective is to investigate the potential relationship between participants’ empathy levels and the changes in their physiological attributes. The empathy levels will be self-reported with questionnaires, and physiological attributes will be measured using different sensors. The main outcome of the study will be a machine learning model to predict a person’s empathy level based on their physiological responses while watching VR videos. Despite the existence of established methodologies and metrics in research and clinical domains, our aim is to contribute to addressing the gap of a universally accepted “gold standard” for assessing empathy. Additionally, we expect to deepen our understanding of the relationship between different emotions and psychological attributes, gender differences in empathy, and the impact of narrative context on empathic responses.
Objavljeno v DiRROS: 23.07.2024; Ogledov: 885; Prenosov: 456
Celotno besedilo (1,12 MB)
Gradivo ima več datotek! Več...

Povzetek: Accurate and comprehensive nursing documentation is essential to ensure quality patient care. To streamline this process, we present SONAR, a publicly available dataset of nursing activities recorded using inertial sensors in a nursing home. The dataset includes 14 sensor streams, such as acceleration and angular velocity, and 23 activities recorded by 14 caregivers using five sensors for 61.7 hours. The caregivers wore the sensors as they performed their daily tasks, allowing for continuous monitoring of their activities. We additionally provide machine learning models that recognize the nursing activities given the sensor data. In particular, we present benchmarks for three deep learning model architectures and evaluate their performance using different metrics and sensor locations. Our dataset, which can be used for research on sensor-based human activity recognition in real-world settings, has the potential to improve nursing care by providing valuable insights that can identify areas for improvement, facilitate accurate documentation, and tailor care to specific patient conditions.
Ključne besede: nursing documentation, nursing activities, SONAR, sensors
Objavljeno v DiRROS: 11.12.2023; Ogledov: 1131; Prenosov: 627
Celotno besedilo (1,62 MB)
Gradivo ima več datotek! Več...

Povzetek: Contact-free sensing gained much traction in the past decade. While remote monitoring of some parameters (heart rate) is approaching clinical levels of precision, others remain challenging (blood pressure). We investigated the feasibility of estimating blood pressure (BP) via pulse transit time (PTT) in a novel remote single-site manner, using a modified RGB camera. A narrow-band triple band-pass filter allowed us to measure the PTT between different skin layers, harvesting information from green and near-infrared wavelengths. The filter minimizes the inter-channel influence and band overlap, however some overlap remains within the filter bands. We further resolve this using a color-channel model and a novel channel-separation method. Using the proposed setup and algorithm, we obtained multi-wavelength (MW) PTTs in an experiment inducing BP changes to 9 subjects. The results showed good absolute Pearson’s correlation coefficient between both MW PTT and systolic BP (R = 0.61, p = 0.08) as well as diastolic BP (R = 0.54, p = 0.05), pointing to feasibility of the proposed novel remote MW BP estimation via PTT. This was further confirmed in a leave-one-subject-out experiment, where a simple Random Forest regression model achieved mean absolute errors of 3.59 and 2.63 mmHg for systolic and diastolic BP respectively.
Objavljeno v DiRROS: 27.07.2023; Ogledov: 1270; Prenosov: 792
Celotno besedilo (1,06 MB)
Gradivo ima več datotek! Več...