CHS: Medium: Behavior360: Learning a Human Behaviorome in Uncontrolled Settings

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With the maturing of mobile sensing, computing, and machine learning, scientists can now build intelligent tools to better understand human behavior by analyzing data from commercial, wearable sensors (such as smartwatches). The goal of this project is to design, build, and evaluate novel algorithms that continuously sense, model, analyze, and interpret human behavior from smartwatch sensor data collected continuously in the wild. Machine learning methods will be designed to infer behavior patterns from collected data as well as generate explanations of behavior patterns that can be easily interpreted by humans with diverse backgrounds. The computational methods will be evaluated using historical data as well as new data collected in free-living environments. The relationship between health and behavior will be explored by using machine learning to derive clinical health scores from collected and modeled sensor data. The research will involve students from diverse disciplinary and demographic backgrounds through involvement in summer research programs and capstone projects.The technical goals of this project are divided into three aims. First, because continuous behavior sensing requires resources that exceed the power capacity of current smartwatches, the investigators will design algorithms that optimize the trade-off between predictive performance and power consumption. Second, the investigators will create robust behavior models that combine sparse label information with sensor data to automatically construct a vocabulary of human behavior, and employ domain adaptation to generalize models across people, times, and behavior types. Third, the investigators will create machine learning methods to produce accurate and interpretable clinical health scores from behavior data with automatically-generated text explanations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.