Metrics for analytics and visualization of big data with applications to activity recognition
Arora, Rohan R
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https://hdl.handle.net/2142/90953
Description
Title
Metrics for analytics and visualization of big data with applications to activity recognition
Author(s)
Arora, Rohan R
Issue Date
2016-04-27
Director of Research (if dissertation) or Advisor (if thesis)
Mehta, Prashant G.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
activity
recognition
metrics
Abstract
Activity recognition systems detect the hidden actions of an agent from sensor measurements made on the agents' actions and the environmental conditions. For such systems, metrics are important for both performance evaluation and visualization purposes. In this thesis, such metrics are developed and illustrated. For human activity recognition datasets, a reporting structure is described to visualize the metrics in a systematic manner. The other contribution of this thesis is to describe a visualization tool for estimating the orientation (attitude) of a rigid body from streaming motion sensor (accelerometer and gyroscope) data. A feedback particle filter (FPF) is implemented algorithmically to solve the estimation problem.
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