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Discusses using the matching pursuit algorithm to characterize time-frequency patterns of body motion in poststroke hemiplegic patients. We have been working on the quantification of body motions in healthy young and elderly subjects, patients with Parkinson's disease (PD), and poststroke hemiplegic (PSH) patients using an accelerometry technique and advanced signal processing methods. In this article, we use the matching pursuit (MP) algorithm to characterize the time-frequency patterns of the acceleration signal recorded from both healthy subjects and poststroke hemilpegic patients. The MP algorithm was chosen since it provides better time and frequency resolutions than other time-frequency analysis methods and is an algorithm that decomposes any signal into several already-known time-frequency patterns, which are called atoms. It also provides detailed information about each time-frequency pattern including its energy, time and frequency localization, and phase and scale (time duration), which can be used for the comparison and the statistical analysis.