A millimeter-scale multifunctional soft gripper provides mechanical and thermal stimulation as well as biosignal sensing.

In this paper, we present an asthma risk prediction tool based on machine learning (ML). The entire tool is implemented on a smartphone as a mobile-health (m-health) application using the resources of Internet-of-Things (IoT). Peak Expiratory Flow Rates (PEFR) are commonly measured using external instruments such as peak flow meters and are well known asthama risk predictors. In this work, we find a correlation between the particulate matter (PM) found indoors and the outside weather with the PEFR. The PEFR results are classified into three categories such as ‘Green’ (Safe), ‘Yellow’ (Moderate Risk) and ‘Red’ (High Risk) conditions in comparison to the best peak flow value obtained by each individual. Convolutional neural network (CNN) architecture is used to map the relationship between the indoor PM and weather data to the PEFR values. The proposed method is compared with the state-of-the-art deep neural network (DNN) based techniques in terms of the root mean square and mean absolute error accuracy measures. These performance measures are better for the proposed method than other methods discussed in the literature. The entire setup is implemented on a smartphone as an app. An IoT system including a Raspberry Pi is used to collect the input data. This assistive tool can be a cost-effective tool for predicting the risk of asthma attacks.

Despite ample research on the association between indoor air pollution and allergic disease prevalence, public health and environmental policies still lack predictive evidence for developing a preventive guideline for patients or vulnerable populations mostly due to limitation of real-time big data and model predictability. Recent popularity of IoT and machine learning techniques could provide enabling technologies for collecting real-time big data and analyzing them for more accurate prediction of allergic disease risks for evidence-based intervention, but the effort is still in its infancy. This pilot study explored and evaluated the feasibility of a deep learning algorithm for predicting asthma risk. It is based on peak expiratory flow rates (PEFR) of 14 pediatric asthma patients visiting the Korea University Medical Center and indoor particulate matter PM10 and PM2.5 concentration data collected at their residence every 10 minutes using a PM monitoring device with a low-cost sensor between September 1, 2017 and August 31, 2018. We interpolated the PEFR results collected twice a day for each patient throughout the day so that it can be matched to the PM and other weather data. The PEFR results were classified into three categories such as ‘Green’ (normal), ‘Yellow’ (mild to moderate exacerbation) and ‘Red’ (severe exacerbation) with reference to their best peak flow value. Long Short-Term Memory (LSTM) model was trained using the first 10 months of the linked data and predicted asthma risk categories for the next 2 months during the study period. LSTM model is found to predict the asthma risk categories better than multinomial logistic (MNL) regression as it incorporates the cumulative effects of PM concentrations over time. Upon successful modifications of the algorithm based on a larger sample, this approach could potentially play a groundbreaking role for the scientific data-driven medical decision making.

This study aims to characterize levels of molds, bacteria, and environmental pollutants, identify the associations between indoor mold and dampness exposures and childhood allergic diseases, including asthma, allergic rhinitis, atopic dermatitis, using three different exposure assessment tools. A total of 50 children with their parents who registered in Seoul and Gyeonggi-do in Korea participated in this study. We collated the information on demographic and housing characteristics, environmental conditions, and lifestyle factors using the Korean version of the International Study of Asthma and Allergies in Childhood questionnaire. We also collected environmental monitoring samples of airborne molds and bacteria, total volatile organic compounds, formaldehyde, and particulate matter less than 10 µm. We evaluated and determined water damage, hidden dampness, and mold growth in dwellings using an infrared (IR) thermal camera and field inspection. Univariate and multivariate regression analyses were performed to evaluate the associations between prevalent allergic diseases and exposure to indoor mold and dampness. Indoor mold and bacterial levels were related to the presence of water damage in dwellings, and the mean levels of indoor molds (93.4 ± 73.5 CFU/m³) and bacteria (221.5 ± 124.2 CFU/m³) in water-damaged homes were significantly higher than those for molds (82.0 ± 58.7 CFU/m³) and for bacteria (152.7 ± 82.1 CFU/m³) in non-damaged dwellings (p < 0.05). The crude odds ratios (ORs) of atopic dermatitis were associated with < 6th floor (OR = 3.80), and higher indoor mold (OR = 6.42) and bacterial levels (OR = 6.00). The crude ORs of allergic diseases, defined as a group of cases who ever suffered from two out of three allergic diseases, e.g., asthma and allergic rhinitis, and allergic rhinitis were also increased by 3.8 and 9.3 times as large, respectively, with water damage (+) determined by IR camera (p < 0.05). The adjusted OR of allergic rhinitis was significantly elevated by 10.4 times in the water-damaged dwellings after adjusting age, sex, and secondhand smoke. Therefore, a longitudinal study is needed to characterize dominant mold species using DNA/RNA-based sequencing techniques and identify a causal relationship between mold exposure and allergic diseases in the future.

Monitoring skin health through parameters like skin hydration (SH) and transepidermal water loss (TEWL) is vital for diagnosing skin conditions and identifying disease factors. Conventional devices and survey-based methods often fail to deliver accurate diagnoses due to circadian rhythms of skin health data, limited measurement frequency, and patient subjectivity. Previous research has shown that prolonged device usage also causes sweat accumulation, compromising reliable monitoring. Here, we present a breathable skin health analyzer (BSA), a wearable device designed for prolonged use, capable of accurate, long-term measurement of SH and TEWL. The BSA addresses considerable obstacles in skin health monitoring by employing a breathable chamber and a bistable actuator that ensures both ventilation and consistent sensor contact with the skin. Validated through a 28-day clinical trial, the BSA and data processing algorithms demonstrated their effectiveness in providing reliable data by analyzing the correlation between particulate matter exposure and the skin barrier health. These results not only highlight the potential to improve the diagnosis and treatment of diseases but also show the possibility of contributing to individual environmental health impact assessments and translational studies.