Early Identification and Visualization of Parkin-sonian Gaits and their Stages Using Convolution Neural Networks and Finite Element Techniques |
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Authors: | Musthaq AHAMED PDSH GUNAWARDANE Nimali T MEDAGEDARA |
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Affiliation: | Department of Mechanical Engineering, Faculty of Engineering Technology, The Open University of Sri lanka;Department of Mechanical Engineering, The University of British Columbia, Vancouver, Canada |
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Abstract: | Parkinson''s Disease (PD) is a neurodegenerative disease which shows a deficiency in dopaminehormone in the brain. It is a common irreversible impairment among elderly people. Identifying this disease in its preliminary stage is im-portant to improve the efficacy of the treatment process. Disordered gait is one of the key indications of early symptoms of PD. Therefore, the present paper introduces a novel approach to identify parkinsonian gait using raw vertical spatiotemporal ground reaction force. A convolution neural network (CNN) is implemented to identify the features in the parkinsonian gaits and their progressive stages. Moreover, the variations of the gait pressures were visually recreated using ANSYS finite element software package. The CNN model has shown a 97% accuracy of recognizing parkinsonian gait and their different stages, and ANSYS model is implemented to visualize the pressure variation of the foot during a bottom-up approach. |
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Keywords: | Convolution Neural Networks Vertical Ground Reaction Force Parkinsonian Gait Finite Element Analysis |
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