Triaxial rehabilitative data analysis incorporating matching pursuit

Abstract

The continuing drive for better rehabilitative healthcare hinges on the availability of sensor data which can be shared and analysed. This leverages on the widespread communications network to provide an integrated health management environment. For this paper, we delineate our current work in sensorizing rehabilitative tests of upper limb movements. Where previously we applied data driven analysis, we now employ time-frequency methods to provide a better analytical basis for our derivations. The use of Matching Pursuit algorithm in biological signals has concentrated on brain signals and much less on human motion. Thus we contribute to efficacy of the algorithm by employing it on rehabilitative data collected from widely available sensors. We describe how we obtained the parameters based on pre-analysing an available data set. By selecting the most useful signal constituents and applying this to signal denoising, we are able to better classify the condition of a patient automatically - which shows encouraging promise in the quest for integrative healthcare.