Effects of natural sound on human stress recovery based on EEG techniques

High-quality indoor air is essential in open-kitchen restaurants for ensuring a healthy workplace and comfortable conditions for visitors. In this study, indoor air quality interdependence between the kitchen and the dining zones in open-kitchen restaurants was analyzed. The method was based on measurements of selected air parameters using a sensor technique and mutual information (MI) analysis. A long-term approach (based on a several-hour time series) and a short-term approach (based on a several-minute time series) were applied. This study involved four open-kitchen restaurants. The indoor conditions were represented by the temperature, relative humidity, CO2 concentration, and content of the total volatile organic compounds (TVOC) in the air. The MI analyses showed that the long-term co-dependence of the indoor conditions between the kitchen and the dining zones was smaller during business hours (MI = 0.12 ÷ 0.40) compared to night hours (MI = 0.24 ÷ 0.58). The ranking of the long-term MI values for the individual air parameters was MICO2 (0.34) ≅ MIT (0.34) > MIRH (0.28) > MITVOC (0.23). The short-term interdependencies were smaller during night hours (median MI = 0.01 ÷ 0.56) compared to business hours (MI = 0.23 ÷ 0.61). Additionally, the short-term MI was subject to high temporal variability. The ranking of the short-term MI values for the individual air parameters was MICO2 (0.48) > MIT (0.46) > MIRH (0.37) > MITVOC (0.26). Due to the weak and highly variable co-dependence of the air parameters between the kitchen and dining areas, multi-zone monitoring of air parameters with an emphasis on TVOC measurements is recommended to ensure proper indoor conditions in open-kitchen restaurants. The presented approach may be applied to design indoor air quality monitoring and ventilation systems not only in open-kitchen restaurants but also in other interiors with functionally different zones.

Co-Dependency of IAQ in Functionally Different Zones of Open-Kitchen Restaurants Based on Sensor Measurements Explored via Mutual Information Analysis.

DWTFrTV-DEAL: Denoising and deep ensemble learning for arrhythmia classification using ECG signals

Machine Learning-Driven Improvements in HRV Artifact Correction for Psychosis Prediction in the Schizophrenia Spectrum

Heartbeat serves as a vital sign of health, aiding the diagnosis of various health issues. The autonomic nervous system (ANS) is responsible for regulating heartbeat, and physical activity (PA) can influence the heart rhythm. Changes in heart rate can signal alterations in PA. However, poor measurements or extreme conditions can result in the loss of heartbeat data, which can negatively impact the analysis of heartbeats. To accurately monitor the Heart Rate Variability (HRV) and cardiovascular health, a proper model to compensate for lost data is necessary. This study investigated the effect of different PAs on InterBeat Interval (IBI) prediction and the possibility of using models trained on unrelated activities to predict the next IBI. The IBI series is divided into piecewise stationary sections based on PA, that is, running, walking, and sitting, as verified by a statistical test. Various machine and deep learning methods have been used to model the IBIs related to specific activities. The models were then used to predict the next IBI for the testing sets of related and unrelated activities, and the error changes were compared for each permutation of training and testing. The models were tested using a Physionet archived dataset. The findings suggest that linear models offer the least prediction error, whereas PA-relevant training could minimize errors in most scenarios. However, in cases where specific PA data are not available, the proposed CNN model demonstrated superior generalization capabilities. These findings can improve HRV error correction techniques and enhance cardiovascular health and ANS monitoring.

Temporal Modeling of Instantaneous Interbeat Interval based on Physical Activity

The Impact of Missing Data on Heart Rate Variability Features: A Comparative Study of Interpolation Methods for Ambulatory Health Monitoring

: Drowsy driving is a major issue in road safety. In this paper, we propose a description of an experimental data collection to develop a drowsiness detection model. The objective of this data collection was mainly to gather physiological data of individuals in simulated driving situations. We designed a realistically annoying scenario to induce fatigue while staying close to real driving conditions. The experiment was run on an augmented reality platform called CAVE. The need for contextualization came early in the design of the experiment. Therefore, in addition to physiological data, we added much more data sources, from driving habits to driving behaviour in addition to self-assessment of fatigue levels and the gold standard (EEG). As a result, this experience helped us create a data set of physiological data completed by elements of context and driving behaviour. Thus allowing us to perform a very rich analysis of these physiological data.

Physiological Data Recording in VR Simulator for Sleepiness Detection During Driving

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The Impact of Missing Data on Heart Rate Variability Features: A Comparative Study of Interpolation Methods for Ambulatory Health Monitoring

Physiological Data Recording in VR Simulator for Sleepiness Detection During Driving