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|Title:||Quantitative Electromyographic and Goniometric Analyses of Normal and Pathological Human Gaits|
|Keywords:||Electrical and Electronics;Electrical and Electronics|
|Abstract:||<p>This thesis is directed generally towards a deeper understanding of normal and pathological biped locomotion. Attention has been focussed mainly on electromyographic (EMG) and kinematic aspects of gait. Emphasizing consistency and repeatability of acquired data, the characteristics of surface EMG signals from m. vastus lateralis and m. rectus femoris during several steps of level walking under controlled repeatable gait conditions at three different speeds for several normal subjects have been studied. The variance ratio, a statistical descriptor for repeatability, has been devised. The findings indicate that the variance ratio offers a simple means for selecting an optimal processor for rectifying and averaging the signals. It is of value also in determining an optimal location for surface electrodes on a muscle.</p> <p>Included in the thesis is original research relating to kinematic information presented in the form of knee-angle/hip-angle diagrams. Cyclic loops which had been utilized in the past mainly for their visual representation have been quantified using a shape recognition technique. Quantitative analyses of angle-angle diagrams associated with 5 normal subjects, an above-knee (A/K) amputee and a cerebral-palsied (C.P.) patient, who had an implanted cerebellar stimulator, were carried out. The quantification and physical interpretations of the parameters extracted from the angle-angle diagrams provided a valuable adjunct to visual assessment of the gaits and elicited significant information regarding overall coordination and control during each gait. Using these methods an in-depth assessment of the efficacy of the cerebellar stimulator implanted in the C.P. patient was accomplished.</p> <p>The thesis also describes an initial attempt, using the methods of time series analysis, at modelling the transfer function between several inputs given by the EMG signals of selected muscle groups and an output which is either the hip or knee angle trajectory. The model was able to suggest which individual muscles were synergistic, whether the inputs chosen were good predictors of the output and the relative contribution of each input in predicting the output.</p>|
|Appears in Collections:||Open Access Dissertations and Theses|
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