Distinguishing Between Human Activities in Real-Time Based on Wearable Sensor Data Using a Low-dimensional Model of Human Movement

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Human Activity Recognition is the process of inferring human activity from motion sensors such as accelerometer and gyroscopes worn on the human body. These motion sensors are embedded in physical activity tracking devices and SmartWatches. Accurate inference of human activity offers many benefits for health monitoring and for promoting overall wellness of an individual. There are many machine learning-based approaches to human activity recognition from sensor data. However, in practice, the current approaches often suffer from poor accuracy on account of uncertainty due to wide range of human body types, sensor locations on the body and real-time sources of uncertainty such as changes in activity speed and intensity, sensor noise, packet drops, sensor failure etc. This award supports fundamental research to provide needed knowledge for the development of robust activity recognition methodology and algorithms. It is projected that there will be a trillion embedded sensors in connected people and devices by 2020. This project's algorithmic and software tools can potentially be directly applied to fitness monitoring, eldercare support, long-term preventive and chronic care, and rehabilitation. Therefore, results from this research will benefit the US economy and society. To promote transitions, several educational initiatives are planned that seek to engage undergraduate students in entrepreneurship. A major objective of the research concerns development of methods and algorithms to mitigate uncertainty in machine learning problems, such as the activity recognition problem, involving dynamic data sets. A control-theoretic framework is planned to not only address the robustness issues due to uncertainty, but also enable certain unified architectures for learning patterns from sensor data. If successful, the work can lead to novel algorithmic approaches to represent, learn and recognize hidden low order patterns in unstructured dynamic data sets. Besides methodological developments, this project will engineer other more tangible outcomes such as the development of algorithms and software for feedback particle filter, enunciation of control architectures and algorithms for representation of complex patterns in data, and development of software tools for the human activity recognition system.