Abstract
In this study, we record 3-axis accelerations during various measurement tests with a smartphone, extract features from the accelerations, and then identify young people and the elderly people, as well as healthy elderly people and care-requiring preliminary group for the features by random forests of machine learning. We also perform the selection of the features specific to the elderly people. For the measurement tests, each subject conducts normal and fast 10m walking, chair standing and sitting test, alternate step test, and balance test with 3 kinds of standing position holding. The subjects are 23 young people and 25 elderly people who can walk independently. As a result, the accuracies in all the measurement tests are 78% or more using selected features, and 91% or more in the alternate step test in particular. Moreover, the amplitude and the dispersion of the acceleration, the frequency component, and the period time are selected as the characteristic peculiar to the elderly people.
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