Continuous monitoring of sensor data from smart phones to identify human activities and gestures, puts a heavy load on the smart phone's power consumption. In this research study, the non-Euclidean geometry of the rich sensor data obtained from the user's smart phone is utilized to perform compressive analysis and efficient classification of human activities by employing machine learning techniques.
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- Partial requirement for: M.S., Arizona State University, 2014Note typethesis
- Includes bibliographical references (p. 50-52)Note typebibliography
- Field of study: Electrical engineering