As part of a large study investigating indoor air in residential houses in Brisbane, Australia, the purpose of this work
was to quantify emission characteristics of indoor particle sources in 15 houses. Submicrometer particle number and
approximation of PM2.5 concentrations were measured simultaneously for more than 48 h in the kitchen of all the
houses by usinga condensation particle counter (CPC) and a photometer (DustTrak), respectively. In addition,
characterizations of particles resultingfrom cookingconducted in an identical way in all the houses were measured by
usinga scanningmobility particle sizer (SMPS), an aerodynamic particle sizer (APS) and a DustTrak. All the events of
elevated particle concentrations were linked to indoor activities usinghouse occupants diary entries, and catalogued
into 21 different types of indoor activities. This enabled quantification of the effect of indoor sources on indoor particle
concentrations as well as quantification of emission rates from the sources. For example, the study found that frying,
grilling, stove use, toasting, cooking pizza, cooking, candle vaporizing eucalyptus oil and fan heater use, could elevate
the indoor submicrometer particle number concentration levels by more than five times, while PM2.5 concentrations
could be up to 3, 30 and 90 times higher than the background levels during smoking, frying and grilling, respectively.
r 2004 Elsevier Ltd. All rights reserved.

Contribution from indoor sources to particle number and mass concentrations in residential houses

Anxiety disorder (AD) is a major mental health illness. However, due to the many symptoms and confounding factors associated with AD, it is difficult to diagnose, and patients remain untreated for a long time. Therefore, researchers have become increasingly interested in non-invasive biosignals, such as electroencephalography (EEG), electrocardiogram (ECG), electrodermal response (EDA), and respiration (RSP). Applying machine learning to these signals enables clinicians to recognize patterns of anxiety and differentiate a sick patient from a healthy one. Further, models with multiple and diverse biosignals have been developed to improve accuracy and convenience. This paper reviews and summarizes studies published from 2012 to 2022 that applied different machine learning algorithms with various biosignals. In doing so, it offers perspectives on the strengths and weaknesses of current developments to guide future advancements in anxiety detection. Specifically, this literature review reveals promising measurement accuracies ranging from 55% to 98% for studies with sample sizes of 10 to 102 participants. On average, studies using only EEG seemed to obtain the best performance, but the most accurate results were obtained with EDA, RSP, and heart rate. Random forest and support vector machines were found to be widely used machine learning methods, and they lead to good results as long as feature selection has been performed. Neural networks are also extensively used and provide good accuracy, with the benefit that no feature selection is needed. This review also comments on the effective combinations of modalities and the success of different models for detecting anxiety.

/pdf/machine-learning-for-anxiety-detection-using-biosignals-a-3jr16edf.pdf

Machine Learning for Anxiety Detection Using Biosignals: A Review

Spatiotemporal distribution characteristics of PM2.5 concentration in China from 2000 to 2018 and its impact on population.

This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six scientific databases, fifty-nine were finally selected according to various criteria established. The systematic review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, pre-processing, processing and feature extraction. Finally, all ML techniques applied to the features of these signals for arousal classification have been studied. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high-performance values. In contrast, it has been shown that unsupervised learning is not present in the detection of arousal through EDA. This systematic review concludes that the use of EDA for the detection of arousal is widely spread, with particularly good results in classification with the ML methods found.

/pdf/machine-learning-techniques-for-arousal-classification-from-16hjy6u5.pdf

Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review

Background Work-related stress is one of the top sources of stress amongst working adults. Relaxation rooms are one organizational strategy being used to reduce workplace stress. Amongst healthcare workers, relaxation rooms have been shown to improve perceived stress levels after 15 min of use. However, few studies have examined physiological and cognitive changes after stress, which may inform why relaxation rooms reduce perceived stress. Understanding the biological mechanisms governing why perceived stress improves when using a relaxation room could lead to more effective strategies to address workplace stress. Objective The purpose of this research study is to understand how physiological measures, cognitive performance, and perceived stress change after acute stress and whether certain sensory features of a relaxation room are more effective at promoting recovery from stress. Methods 80 healthy adults will perform a stress induction task (Trier Social Stress Test, TSST) to evaluate how physiological and cognitive responses after stress are affected by sensory features of a relaxation room. After the stress induction task, participants will recover for 40 min in a MindBreaks™ relaxation room containing auditory and visual stimuli designed to promote relaxation. Participants will be randomized into four cohorts to experience auditory and visual stimuli; auditory stimuli; visual stimuli; or no stimuli in the room. Measures of heart rate and neural activity will be continuously monitored using wearable devices. Participants will perform working memory assessments and rate their perceived stress levels throughout the experiment. These measures will be compared before and after the stress induction task to determine how different sensory stimuli affect the rate at which individuals recover. Results Recruitment started in December 2021 and will continue until December 2022 or until enrollment is completed. Final data collection and subsequent analysis are anticipated by December 2022. We expect all trial results will be available by early 2023. Discussion Findings will provide data and information about which sensory features of a relaxation room are most effective at promoting recovery after acute stress. This information will be useful in determining how these features might be effective at creating individualized and organizational strategies for mitigating the effects of workplace stress.

Investigating How Auditory and Visual Stimuli Promote Recovery After Stress With Potential Applications for Workplace Stress and Burnout: Protocol for a Randomized Trial

Healthy home interventions: Distribution of PM2.5 emitted during cooking in residential settings

Workplace-related stressors, economic strain, and lack of access to educational and basic needs have exacerbated feelings of stress in the United States. Ongoing stress can result in an increased risk of cardiovascular, musculoskeletal, and mental health disorders. Similarly, workplace stress can translate to a decrease in employee productivity and higher costs associated with employee absenteeism in an organization. Detecting stress and the events that correlate with stress during a workday is the first step to addressing its negative effects on health and wellbeing. Although there are a variety of techniques for stress detection using physiological signals, there is still limited research on the ability of behavioral measures to improve the performance of stress detection algorithms. In this study, we evaluated the feasibility of detecting stress using deep learning, a subfield of machine learning, on a small data set consisting of electrodermal activity, skin temperature, and heart rate measurements, in combination with self-reported anxiety and stress. The model was able to detect stress periods with 96% accuracy when using the combined wearable device and survey data, compared to the wearable device dataset alone (88% accuracy). Creating multi-dimensional datasets that include both wearable device data and ratings of perceived stress could help correlate stress-inducing events with feelings of stress at the individual level and help reduce intra-individual variabilities due to the subjective nature of the stress response.

/pdf/the-feasibility-of-wearable-and-self-report-stress-detection-4grnepytro.pdf

Nadia Wood

Papers

Healthy home interventions: Distribution of PM2.5 emitted during cooking in residential settings

The Feasibility of Wearable and Self-Report Stress Detection Measures in a Semi-Controlled Lab Environment