A Comprehensive Analysis of Human Gait for Abnormal Foot Recognition Using Neuro-Genetic approach
In the present paper, Abnormal Foot Recognition (AFR) (one of the biometrical research problem) has been carried out using knowledge-based model. Most of the work in this area has been carried out for the detection of various problems like: short and long foot problems, ankle and knee angle variations, detection of walking speed after hitting an object, recovery type of problems using GAIT analysis and so on. Very little amount of work has been carried out with knowledge-based or model-based using soft-computing tools like artificial neural network (ANN) and genetic algorithm (GA). In the present paper, neuro-genetic approach has been applied for AFR. The work has been carried out in two phases: modeling phase and understanding phase. In modeling phase, a knowledge-based model called GAIT_MODEL has been formed using ANN. To perform the first phase of the work, a known gait image has been fed as input. Then it has been enhanced from distortions and compressed with loss-less information. Later on it has been segmented along with the detection of the region of interest. Hence the relevant geometrical parameters of GAIT have been extracted from that ROI and stored in a corpus.. In the second phase, the GAIT_MODEL has been understood for AFR using genetic algorithm. It has been done by considering an unknown gait image for the recognition of abnormalities in the foot. For this an algorithm known as NGBAFR (Neuro-Genetic based Abnormal Foot Recognition) has been proposed in the present paper and has been tested with 20 subjects on a flat and plain surface without carrying any weights. The result has been found very satisfactory with the tested data sets.
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