The continuing drive for better rehabilitative healthcare hinges on the availability of sensor data which can be shared and analysed. This leverages on the w idespread 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 w e 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 w e 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.

In rehabilitation, continual assessment of those with disabilities is needed to determine the effectiveness of therapy and to prescribe the regimen and intensity of future treatment. Conducting assessments is challenging -there is a need to maintain objectivity and consistency across time. Also, repetitious tests can lull the assessor into lower levels of alertness. These motivate for automated scoring of rehabilitative tests.In this paper, we describe our work in automating the widely used and accepted Action Research Arm Test. We focus on the grasp subtest which employs a cube into which we embed sensors. Previously we have used live patient simulators and now the full set of patient trials have been completed.We employ Singular Spectrum Analysis on the signals, for which the resulting eigenvalues are then selected in a principled way to aid in signal filtering. The results show encouraging promise in our quest for automated scoring.

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