Training Data Generation Using Human Link Model for State Estimation of Care Robot User

The importance of care robots is growing owing to the increasing numbers of the elderly and the shortage of caregivers. For robots to automatically assist the movements of the elderly users, the state of their pose should be estimated. Hence, we proposed a method for estimating the user’s state using candidate positions of the center of gravity of the robot user using a few sensors. In this method, sensor outputs in all states and movements of the user are collected in advance, and a state estimation and abnormality detection model created by machine learning using the relationship between the outputs and state is used as training data. However, it is difficult to collect training data pertaining to elderly people, before use, for this purpose. Therefore, a database of human actions is created for use as the training data. Rich databases of such actions performed sans robots already exist, but not where care robots are used. Conventional methods for simulating human body movements to estimate human states require actual data and expert coordination and do not address detailed state estimation for physical support. Therefore, this study proposed a method for generating training data using a human link model, which enables the creation of a model that can estimate the state of a robot user without requiring the elderly user to stand up and perform other robot-assisted actions before use. Training data is generated using a link model and candidate centers of gravity, a simple method by which the state of the care robot user can be estimated, and physical support for standing, walking, and sitting can be provided. The effectiveness of the state estimation using the link model generated training data is verified off-line using sensor data independently obtained from the actual movements of the robot and user and also through experiments using an actual care robot. The results validated the state estimation since the time error was sufficiently short (0.35–1.85s), and the experiment confirmed that the robot could realize assistive actions with 90% accuracy.