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Geometry Aware Compressive Analysis of Human Activities : Application in a Smart Phone Platform


Abstract 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. We are interested in the generalization of classical tools for signal approximation to newer spaces, such as rotation data, which is best studied in a non-Euclidean setting, and its application to activity analysis. Attributing to the non-linear nature of the rotation data space, which involve a heavy overload on the smart phone's pr... (more)
Created Date 2014
Contributor Sivakumar, Aswin (Author) / Turaga, Pavan (Advisor) / Spanias, Andreas (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / CPU Usage / Human Activity Recognition / Non Euclidean Geometry / Symbolic Representation / Unit Quaternions / Unit sphere- S-3 manifold
Type Masters Thesis
Extent 61 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note M.S. Electrical Engineering 2014
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis