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Exposure assessment studies are the primary means for understanding links between exposure to chemical and physical agents and adverse health effects. Recently, researchers have proposed using wearable monitors during exposure assessment studies to obtain higher fidelity readings of exposures actually experienced by subjects. However, limited research has been conducted to link a wearer's actions to periods of exposure, a necessary step for estimating inhaled dosage. To aid researchers in these settings, we developed a machine learning model for identifying periods of bicycling activity using passively collected data from the RTI MicroPEM wearable exposure monitor, a lightweight device capable of continuously sampling both air pollution levels and accelerometry parameters. Our best performing model identifies biking activity with a mean leave-one-session-out (LOSO) cross-validation F1 score of 0.832 (unweighted) and 0.979 (weighted). Accelerometer derived features contributed greatly to the model performance, as well as temporal smoothing of the predicted activities. Additionally, we found competitive activity recognition can occur with even relatively low sampling rates, suggesting suitability for exposure assessment studies where continuous data collection for long periods (without recharge) are needed to capture realistic daily routines and exposures. [ABSTRACT FROM AUTHOR]

While governments, researchers, and NGOs are exploring ways to leverage big data sources for sustainable development, household surveys are still a critical source of information for dozens of the 232 indicators for the Sustainable Development Goals (SDGs) in low- and middle-income countries (LMICs). Though some countries' statistical agencies maintain databases of persons or households for sampling, conducting household surveys in LMICs is complicated due to incomplete, outdated, or inaccurate sampling frames. As a means to develop or update household listings in LMICs, this paper explores the use of machine learning models to detect and enumerate building structures directly from satellite imagery in the Kaduna state of Nigeria. Specifically, an object detection model was used to identify and locate buildings in satellite images. In the test set, the model attained a mean average precision (mAP) of 0.48 for detecting structures, with relatively higher values in areas with lower building density (mAP = 0.65). Furthermore, when model predictions were compared against recent household listings from fieldwork in Nigeria, the predictions showed high correlation with household coverage (Pearson = 0.70; Spearman = 0.81). With the need to produce comparable, scalable SDG indicators, this case study explores the feasibility and challenges of using object detection models to help develop timely enumerated household lists in LMICs. [ABSTRACT FROM AUTHOR]