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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

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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    Date Created
    • 2014
    Resource Type
  • Text
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    Note
    • Partial requirement for: M.S., Arizona State University, 2014
      Note type
      thesis
    • Includes bibliographical references (p. 50-52)
      Note type
      bibliography
    • Field of study: Electrical engineering

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    by Aswin Sivakumar

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