Two-Step Wavestrapping: Simulating Non-Stationary Acceleration Data in the Mobile Computing Context

Files
06-01.pdf (1.25 MB)
Author(s)
Yi, Ji Soo
Jung, Yoon Young
Jacko, Julie A.
Sainfort, François
Advisor(s)
Editor(s)
Associated Organization(s)
Organizational Unit
Wallace H. Coulter Department of Biomedical Engineering
The joint Georgia Tech and Emory department was established in 1997
Organizational Unit
Series
Collections
Research Publications
Supplementary to:
Permanent Link
http://hdl.handle.net/1853/25839
Abstract
Measurements of acceleration provide an important source of information in understanding and predicting the movements of mobile computer users. However, collecting acceleration data can require substantial time and resources because it involves both human participation and the construction of adequate contextual environments. Thus, simulating acceleration data would be helpful to circumvent these difficulties. However, using traditional resampling techniques turned out to be inadequate for simulation of these non-stationary time series data. The present study proposes a wavelet-based resampling approach, called the “two-step wavestrapping,” which consists of parallel wavestrapping and energy/trend adjustments. This approach was applied to the acceleration data we collected from mobile computing users under six different contextual settings. The results showed that two-step wavestrapping can successfully generate surrogate acceleration data from the collected acceleration data.
Sponsor
Date
2005-12-16
Extent
Resource Type
Text
Resource Subtype
Technical Report
Rights Statement
Rights URI