Tenth International Conference On Advances In Computing, Control And Networking - ACCN 2020
Author(s) : Atsushi Hagihara, Fumiko Harada, Hiromitsu Shimakawa
In this paper, we propose a method of estimating the care recipient's posture on the bed in detail and predicting the care recipient's getting out of the bed. In order to grasp the movement and posture of the care recipient on the bed in real time, a classifier that estimates the posture of the care recipient on the bed is created by machine learning. The explanatory variables were the position of the center of gravity of each part of the care recipient, the average value of the velocity and acceleration of the position of the center of gravity over a certain period of time, and the variance and covariance of the position of the center of gravity, and 16 types of body positions were classified. As a result of Random Forest, we were able to classify with an F value of 0.72 or more. This suggests that it is possible to estimate the getting out of the bed of a care recipient in real time.