Metrics for analytics and visualization of big data with applications to activity recognition

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2016

Author dc:creator

• Arora, Rohan R

Contributors dc:contributor

• Mehta, Prashant G.

• activity
• recognition
• metrics

Statement dc:rights

• 2016 Rohan R. Arora

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/90953

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/90953