Daily Associations of Air Pollution and Pediatric Asthma Risk Using the Biomedical REAI-Time Health Evaluation (BREATHE) Kit.

Background: Exposure to air pollution is associated with acute pediatric asthma exacerbations, including reduced lung function, rescue medication usage, and increased symptoms; however, most studies are limited in investigating longitudinal changes in these acute effects. This study aims to investigate the effects of daily air pollution exposure on acute pediatric asthma exacerbation risk using a repeated-measures design. Methods: We conducted a panel study of 40 children aged 8−16 years with moderate-to-severe asthma.

Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors' Data.

Background: Time-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration.

Current respiratory symptoms and risk factors in pregnant women cooking with biomass fuels in rural Ghana.

Background: More than 75% of the population in Ghana relies on biomass fuels for cooking and heating. Household air pollution (HAP) emitted from the incomplete combustion of these fuels has been associated with adverse health effects including respiratory effects in women that can lead to chronic obstructive pulmonary disease (COPD), a major contributor to global HAP-related mortality. HAP is a modifiable risk factor in the global burden of disease, exposure to which can be reduced.

Children Activity Recognition: Challenges and Strategies.

In this paper, we study the problem of children activity recognition using smartwatch devices. We introduce the need for a robust children activity model and challenges involved. To address the problem, we employ two deep neural network models, specifically, Bi-Directional LSTM model and a fully connected deep network and compare the results to commonly used models in the area.

CHILDHOOD RESPIRATORY MORBIDITY AND COOKING PRACTICES AMONG HOUSEHOLDS IN A PREDOMINANTLY RURAL AREA OF GHANA.

Background: Household air pollution is a leading risk factor for respiratory morbidity and mortality in developing countries where biomass fuel is mainly used for cooking.

Materials And Method: A household cross-sectional survey was conducted in a predominantly rural area of Ghana in 2007 to determine the prevalence of respiratory symptoms and their associated risk factors. Household cooking practices were also assessed as part of the survey.

Personal exposures to fine particulate matter and black carbon in households cooking with biomass fuels in rural Ghana.

Objective: To examine cooking practices and 24-h personal and kitchen area exposures to fine particulate matter (PM2.5) and black carbon in cooks using biomass in Ghana.

Methods: Researchers administered a detailed survey to 421 households.