Gait analysis continues to be an important technique for many clinical applications to diagnose and monitor certain diseases. Many mental and physical abnormalities cause measurable differences in a person's gait. Gait analysis has applications in sport, computer games, physical rehabilitation, clinical assessment, surveillance, human recognition, modeling, and many other fields. There are established methods using various sensors for gait analysis, of which accelerometers are one of the most often employed. Accelerometer sensors are generally more user friendly and less invasive. In this paper, we review research regarding accelerometer sensors used for gait analysis with particular focus on clinical applications. We provide a brief introduction to accelerometer theory followed by other popular sensing technologies. Commonly used gait phases and parameters are enumerated. The details of selecting the papers for review are provided. We also review several gait analysis software. Then we provide an extensive report of accelerometry-based gait analysis systems and applications, with additional emphasis on trunk accelerometry. We conclude this review with future research directions.

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.

An adaptive switching median filter (ASMF) is presented in this paper. Extensive simulation shows that it can provide very high quality restored images for images that are contaminated by "salt & pepper" noise, especially when the noise density is large. Comparing with other median filtering methods, the ASMF can effectively remove "salt & pepper" noise even when the majority of pixels in the filtering window are noise pixel or all of the pixels are noise pixel.