Discrimination of Old/Young Persons from Acceleration Data during Walking Based on Neural Networks

摘要

An analytical algorithm based on neural networks is proposed for the problem of discrimination of seven subjects into the two groups of old persons and young persons on the basis of acceleration data during walking. An old person model and a young person model are constructed using neural networks, and subjects are discriminated by comparison of the degree to which the subject data match the models. To improve discrimination accuracy, a method is further proposed in which data that clearly manifest a difference in the degree of matching are added to learning data, and model reconstruction is iterated. Frequency analysis is also used to extract and quantify a feature from data on old persons and young persons and to discriminate this characteristic quantity by comparison of the degree of similarity between subjects and old persons/young persons. Validity of the discrimination results based on neural networks is examined by comparison with the results of frequency analysis.