Browsing by Author "Chappell, Paul H."
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Item Open Access EXPERIMENTAL LEAD ZIRCONATE TITANATE (PZT) SLIP SENSOR(2008) Chappell, Paul H.; Cotton, Darryl P.J.; Cranny, Andy; White, Neil M.Future advanced artificial hands will require the automatic holding of objects using feedback control. To achieve this aim will require sensors of various types, one of which should be capable of detecting the relative movement between the surface of a grasped object and the hand (slip). A low-cost sensor, using thick-film technology, has been developed which detects slip using the piezoelectric effect. Experimental evaluation of the sensor has been carried out using a test apparatus whereby a block of aluminium representing an object slides past the sensor. Attached to the object surface is a Perspex sheet with repeating groves cut into the surface. Two different separations of the groves have been tested. The results show that the slip sensor detects the relative velocity between a moving object and the sensor surface. The sensor has a frequency response into the kilohertz which makes it an excellent candidate for a slip sensor. The sensor is able to detect slip with and without a cosmetic material covering the sensor. Computer simulations of the mechanical modes of vibration have shown that the frequency of the lowest fundamental mode is much higher than the electronic signal output from the sensor.Item Open Access SURFACE EMG CLASSIFICATION USING MOVING APPROXIMATE ENTROPY AND FUZZY LOGIC FOR PROSTHESIS CONTROL(2008) Ahmad, Siti A.; Chappell, Paul H.Electromyographic control systems based on pattern recognition have become an established technique in upper limb prosthetic control application. This paper describes a use of fuzzy logic to discriminate different hand grip postures by processing the surface EMG from wrist muscles. A moving data window of two hundred values is applied to the SEMG data and a new method called moving approximate entropy is used to extract information from the signals. The analyses show differences at three states of contraction (start, middle and end) where significant dips can be observed at the start and end of a muscle contraction. Mean absolute value (MAV) and kurtosis are also used in the extraction process to increase the performance of the system. The extracted features are fed to a fuzzy logic system to be classified and select the output appropriately. The preliminary experimental result demonstrates the ability of the system to classify the features related to different grip postures.