School of Public Health

Permanent URI for this communityhttp://hdl.handle.net/1903/1633

The collections in this community comprise faculty research works, as well as graduate theses and dissertations.

Note: Prior to July 1, 2007, the School of Public Health was named the College of Health & Human Performance.

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Subject: search.filters.subject.machine learning

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    Gender Effects on Knee Loading and Prediction of Knee Loads Using Instrumented Insoles and Machine Learning
    (2024) Snyder, Samantha Jane; Miller, Ross H.; Shim, Jae Kun; Kinesiology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Women are more likely to experience knee osteoarthritis as compared to men, but the underlying mechanisms behind this disparity are unclear. Greater knee loads, knee adduction moment, knee flexion moment, and medial joint contact force, are linked to severity and progression of knee osteoarthritis. However, it is unknown if greater knee loads in healthy, young women during activities of daily living (sit-to-stand, stand-to-sit, walking and running) can partially explain the higher prevalence of knee osteoarthritis rates in women. Although previous research showed no significant differences in peak knee adduction moment and knee flexion moment between men and women, differences in peak medial joint contact force are largely unexplored. Women also tend to take shorter steps and run slower than men. It is unknown if these differences may result in greater cumulative knee loading per unit distance traveled as compared to men. Furthermore, knee loading measurement is typically confined to a gait laboratory, yet the knee is subjected to large cyclical loads throughout daily life. The combination of machine learning techniques and wearable sensors has been shown to improve accessibility of biomechanical measurements without compromising accuracy. Therefore, the goal of this dissertation is to develop a framework for measuring these risk factors using machine learning and novel instrumented insoles, and to investigate differences in peak and cumulative per unit distance traveled knee loads between young, healthy men and women. In study 1 we developed instrumented insoles and examined insole reliability and validity. In study 2, we estimated knee loads for most activities with strong correlation coefficients and low to moderate mean absolute errors. In study 3, we found peak medial joint contact force was not significantly different across activities for men and women. Similarly, in study 4, we found no significant difference between men and women in knee loads per unit distance traveled during walking and running. These findings suggest biomechanical mechanisms alone cannot explain the disproportionate rate of knee osteoarthritis in women. However, in future research, the developed knee loading prediction models can help quantify daily knee loads and aid in reducing knee osteoarthritis risk in both men and women.
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    Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases
    (MDPI, 2020-09-01) Nguyen, Quynh C.; Huang, Yuru; Kumar, Abhinav; Duan, Haoshu; Keralis, Jessica M.; Dwivedi, Pallavi; Meng, Hsien-Wen; Brunisholz, Kimberly D.; Jay, Jonathan; Javanmardi, Mehran; Tasdizen, Tolga
    The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents’ risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.
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    Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes
    (MDPI, 2022-09-24) Yue, Xiaohe; Antonietti, Anne; Alirezaei, Mitra; Tasdizen, Tolga; Li, Dapeng; Nguyen, Leah; Mane, Heran; Sun, Abby; Hu. Ming; Whitaker, Ross T.; Nguyen, Quynh C.
    Built environment neighborhood characteristics are difficult to measure and assess on a large scale. Consequently, there is a lack of sufficient data that can help us investigate neighborhood characteristics as structural determinants of health on a national level. The objective of this study is to utilize publicly available Google Street View images as a data source for characterizing built environments and to examine the influence of built environments on chronic diseases and health behaviors in the United States. Data were collected by processing 164 million Google Street View images from November 2019 across the United States. Convolutional Neural Networks, a class of multi-layer deep neural networks, were used to extract features of the built environment. Validation analyses found accuracies of 82% or higher across neighborhood characteristics. In regression analyses controlling for census tract sociodemographics, we find that single-lane roads (an indicator of lower urban development) were linked with chronic conditions and worse mental health. Walkability and urbanicity indicators such as crosswalks, sidewalks, and two or more cars were associated with better health, including reduction in depression, obesity, high blood pressure, and high cholesterol. Street signs and streetlights were also found to be associated with decreased chronic conditions. Chain link fence (physical disorder indicator) was generally associated with poorer mental health. Living in neighborhoods with a built environment that supports social interaction and physical activity can lead to positive health outcomes. Computer vision models using manually annotated Google Street View images as a training dataset were able to accurately identify neighborhood built environment characteristics. These methods increases the feasibility, scale, and efficiency of neighborhood studies on health.