Early Identification and Visualization of Parkin-sonian Gaits and their Stages Using Convolution Neural Networks and Finite Element Techniques

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.     

Optimal replacement policy for general parallel reliability system

Recently Nakagawa (1979) has developed an optimal replacement policy for a parallel system operating in a random environment based on the model given by Rade (1976). The optimization policy (Nakagawa 1979) uses the idea of MTBF (mean time before failure) performance and is limited to systems comprised of ‘identical units’. We observe that relaxing the assumption of identical units and working with ‘general systems’ (i.e. systems comprised of not necessarily identical components) has several operational and practical advantages (Venugopal and Ahamed 1986 a, b, Venugopal 1987). Accordingly, an optimal replacement policy for a typical general parallel system is developed in this paper. Earlier results are recovered as special cases. Illustrative numerical work highlighting the applicational scope is also presented.