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Robust health stream processing




Ericson, Kathleen, author
Pallickara, Shrideep, advisor
Massey, Daniel, committee member
Turk, Daniel, committee member
Anderson, Charles, committee member

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As the cost of personal health sensors decrease along with improvements in battery life and connectivity, it becomes more feasible to allow patients to leave full-time care environments sooner. Such devices could lead to greater independence for the elderly, as well as for others who would normally require full-time care. It would also allow surgery patients to spend less time in the hospital, both pre- and post-operation, as all data could be gathered via remote sensors in the patients home. While sensor technology is rapidly approaching the point where this is a feasible option, we still lack in processing frameworks which would make such a leap not only feasible but safe. This work focuses on developing a framework which is robust to both failures of processing elements as well as interference from other computations processing health sensor data. We work with 3 disparate data streams and accompanying computations: electroencephalogram (EEG) data gathered for a brain-computer interface (BCI) application, electrocardiogram (ECG) data gathered for arrhythmia detection, and thorax data gathered from monitoring patient sleep status.


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interference detection
health stream processing
stream processing
distributed systems


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