Event identification in movement recordings by means of qualitative patterns.

We present a pattern-matching technique for detecting events in movement recordings. The events are defined as sequences of qualitative changes in the speed and/or the higher order derivatives (e.g., in a speed peak, the acceleration changes from positive to negative). The technique uses qualitative patterns that are sequences of qualitative states (e.g., negative, infinitesimal, positive...) of the speed and the higher order derivatives. A fast pattern-matching algorithm is presented. Its sensitivity can be tuned by means of a filtering parameter, and a multiscale analysis method is proposed for detecting events of different amplitudes and durations. An application to the assessment of the irregularity of rapid movement in Parkinson's disease is presented.