In physical medicine and rehabilitation, it is important to be able to collect information regarding a patient's behavior and range of mobility throughout their daily activities. Grab bars are used widely in the homes of individuals with mobility impairments so their usage while performing physical tasks can provide valuable information as to the individual's physical status. This paper explores the extraction of location information for forces applied to a grab bar embedded with a nonlinear pressure sensor array of low spatial resolution. It first describes the instrumentation of the grab bar and the calibration procedure. It then investigates three methods of estimating the contact location; a simple centroid, a percentage-based lookup table and an artificial neural network. Results of the three methods are reported based on data collected from different forces and contact locations applied along the bar. The artificial neural network proves to be the most successful method of estimating the points of contact, by most accurately modeling the nonlinearities in the system. ©2008 IEEE.
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