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. A small sliding window can produce noisy predictions under stable conditions, whereas a large sliding window may miss brief bursts of intense activity. Objective: We aimed to create an HAR framework adapted to variable duration activity bouts by (1) detecting the change points of activity bouts in a multivariate time series and (2) predicting activity for each homogeneous window defined by these change points. Methods: We applied standard fixed-width sliding windows (4-6 different sizes) or greedy Gaussian segmentation (GGS) to identify break points in filtered triaxial accelerometer and gyroscope data. After standard feature engineering, we applied an Xgboost model to predict physical activity within each window and then converted windowed predictions to instantaneous predictions to facilitate comparison across segmentation methods. We applied these methods in 2 datasets: the human activity recognition using smartphones (HARuS) dataset where a total of 30 adults performed activities of approximately equal duration (approximately 20 seconds each) while wearing a waist-worn smartphone, and the Biomedical REAl-Time Health Evaluation for Pediatric Asthma (BREATHE) dataset where a total of 14 children performed 6 activities for approximately 10 min each while wearing a smartwatch. To mimic a real-world scenario, we generated artificial unequal activity bout durations in the BREATHE data by randomly subdividing each activity bout into 10 segments and randomly concatenating the 60 activity bouts. Each dataset was divided into ~90% training and ~10% holdout testing. Results: In the HARuS data, GGS produced the least noisy predictions of 6 physical activities and had the second highest accuracy rate of 91.06% (the highest accuracy rate was 91.79% for the sliding window of size 0.8 second). In the BREATHE data, GGS again produced the least noisy predictions and had the highest accuracy rate of 79.4% of predictions for 6 physical activities. Conclusions: In a scenario with variable duration activity bouts, GGS multivariate segmentation produced smart-sized windows with more stable predictions and a higher accuracy rate than traditional fixed-size sliding window approaches. Overall, accuracy was good in both datasets but, as expected, it was slightly lower in the more real-world study using wrist-worn smartwatches in children (BREATHE) than in the more tightly controlled study using waist-worn smartphones in adults (HARuS). We implemented GGS in an offline setting, but it could be adapted for real-time prediction with streaming data.

Advances in measurement technology are producing increasingly time-resolved environmental exposure data. We aim to gain new insights into exposures and their potential health impacts by moving beyond simple summary statistics (e.g., means, maxima) to characterize more detailed features of high-frequency time series data. This study proposes a novel variant of the Self-Organizing Map (SOM) algorithm called Dynamic Time Warping Self-Organizing Map (DTW-SOM) for unsupervised pattern discovery in time series. This algorithm uses DTW, a similarity measure that optimally aligns interior patterns of sequential data, both as the similarity measure and training guide of the neural network. We applied DTW-SOM to a panel study monitoring indoor and outdoor residential temperature and particulate matter air pollution (PM_2.5) for 10 patients with asthma from 7 households near Salt Lake City, UT; the patients were followed for up to 373 days each. Compared to previous SOM algorithms using timestamp alignment on time series data, the DTW-SOM algorithm produced fewer quantization errors and more detailed diurnal patterns. DTW-SOM identified the expected typical diurnal patterns in outdoor temperature which varied by season, as well diurnal patterns in PM_2.5which may be related to daily asthma outcomes. In summary, DTW-SOM is an innovative feature engineering method that can be applied to highly time-resolved environmental exposures assessed by sensors to identify typical diurnal (or hourly or monthly) patterns and provide new insights into the health effects of environmental exposures.

This study develops a linear regression model to select local, low-collinear explanatory variables. This model combines two well-known models: geographically weighted regression (GWR) and elastic net (EN). The GWR model posits that the regression coefficients vary as a function of location and focuses on solving the problem of explaining the relationships under the spatially nonstationary condition, which a global model cannot solve. GWR cannot fulfill the task of variable selection, however, which is problematic when there are many explanatory variables with nonnegligible multicollinearity. On the other hand, the EN model is a member of the regulated regression family. EN can trim the number of explanatory variables and select the most important ones by adding penalty terms in its cost function, and it has been proven to be robust under the high-multicollinearity condition. The EN model is a global model, however, and does not consider the spatial nonstationarity. To overcome these deficiencies, we proposed the geographically weighted elastic net (GWEN) model. GWEN uses the kernel weights derived from GWR and applies EN locally to select variables for each geographical location. The result is a set of locally selected, low-collinear explanatory variables with spatially varying coefficients. We demonstrated the GWEN method on a data set relating population changes to a set of social, economic, and environmental variables in the Lower Mississippi River Basin. The results show that GWEN has the advantages of both the high prediction accuracy of GWR and the low multicollinearity among explanatory variables of EN.

The present study aims to explore the spatial and temporal changes and drivers of carbon emission patterns in China, with the aim of encouraging county-level carbon reduction policies in pursuit of sustainable development. To this end, we have studied the spatial disparities, spatio-temporal patterns, and evolution characteristics of carbon emissions using county-level carbon emissions data from China between 2002 and 2017. Additionally, we have comprehensively considered the dynamic impacts of both county-level and city-level environmental factors on carbon emissions based on an optimized hierarchical random forest model. The results show that the carbon emissions of China’s counties have generally followed an upward trend before stabilizing. Notable characteristics include elevated carbon emissions in the northern regions and reduced carbon emissions in the southern areas. Additionally, there are higher carbon emissions in the eastern regions compared to lower emissions in the western and inland areas, with discernible local clustering patterns. These findings underscore the importance of tailoring the government’s emission reduction strategy to address the phased variations in carbon emissions across different districts and counties. It is essential to emphasize the key role of major urban agglomerations and metropolitan areas in carbon emission reduction, while also addressing potential emission sources in the resource-rich, yet technologically disadvantaged, northwest region. Furthermore, improving energy efficiency through technological innovation should be the primary means of carbon emission reduction at the county level.

Superblocks can help to transform urban areas into pedestrian-centric neighborhoods. First launched in Barcelona, Spain, they are expected to reduce harmful environmental exposures, increase green space access and thereby provide substantial health benefits. However, few studies have examined the practicality and likely benefits of implementing Superblocks in other metropolitan areas. We developed a methodological framework to build a generalizable City of Los Angeles (LA) Superblocks Model and evaluate the predicted health benefits that would follow such an intervention. We derived and used five rules to guide the choice of arterial streets and candidate blocks and the choice of major bounding streets that could facilitate mobility across the metropolitan area. We next used the BenMap-CE model to perform a quantitative assessment of the health and economic benefits that would accompany five scenarios that would transform 5–50% of the residential areas in the City of LA to Superblocks. We found that the creation of superblocks resulted in significant reductions in hospital admissions and significant economic savings. The benefits were strongest when 5–10% of residential areas were transformed, but rapidly decreased as the threshold reached 30%. These results will help stakeholders determine the optimal balance between reduced car traffic and improved health outcomes. Moreover, we illustrated how to develop a Superblocks model for a highly versatile and populated metropolitan area like the City of LA and how the model can be used to assess the potential health benefits and benchmark the relationship between the scale of the Superblock implementation and the accompanying health benefits moving forward.

With remarkable progress being witnessed in recent years in the development of sensors, these advances in sensor technology provide unprecedented opportunities for (1) the early diagnosis and prevention of human diseases by detecting critical biomarkers; (2) health assessments by monitoring and analyzing human physiological signals in healthcare and biomedical applications; and (3) the efficient evaluation of human-health-relevant environmental factors by monitoring and measuring environmental determinants