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Study of Plantar-Pressure Recognition Systems with Machine-Learning Methods
In recent years, the biometric technology has become more and more popular, and this makes authentication method is no longer limited to the way which only using account and password. It makes our life become not only convenient but secure. However, in the mass biometric market, the balance of system cost and identification accuracy are always been one of the key issues that the identification system is difficult to popularize. Previous study shows, the rate of using feature extraction and machine learning is not taking the high proportion than now. And there are few studies in research of the cost of data training. Because of this, our study is mainly for research and analyze the characteristics of foot pressure information, combining with machine learning to create a system module that can quickly train data and perform a high accuracy. And then based on the system identification accuracy and the sensor sensing condition, we can adjust the number of sensors to achieves the goal of cost saving. The result shows that the system not only performs good results in the identification accuracy but also saves the training and identification time. The cost is also more cheaper than before, and it will make the biometric technology become more popularization.
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