Standing, Walking, and Sitting Support Robot Based on User State Estimation Using a Small Number of Sensors

Abstract

With the aging of the population and the consequent severe shortage of caregivers, the demand for care robots to assist the elderly is increasing. However, care robots have yet to be widely adopted owing to cost constraints and anxiety issues due to several factors. For instance, care robots are required to have higher functionality than general care devices. It is important to provide both massive power and the appropriate support for the user’s state. However, this requires more sensors to obtain detailed information for user-state estimation and more actuators for physical support, increasing the cost and risk of failure. In a system that has many sensors and operates based on detailed data, the problem of user privacy also emerges. The risk of personal information leakage and the feeling of being monitored increase user discomfort. To support standing up and prevent falling during walking, care robots are required to apply power to the user according to the user state. The position of the center of gravity (CoG) has been used for such state estimation; however, many sensors are required to determine the accurate CoG position. To reduce the number of sensors required for user state estimation, we proposed a method for calculating CoG candidates, and validated the proposed method via experiments. Previous studies have focused solely on normal standing-up motion. However, in daily activities, standing up, walking, and sitting down are a set of motions. In addition, it is not always true that the care robot user can move normally; hence, anomaly detection is beneficial in care robots. Therefore, it is important to estimate the user state considering not only standing-up motion, but also walking and sitting down, as well as any anomaly that may occur during these motions. In this study, we develop an elderly support system that can assist in standing, walking, and sitting based on user state estimation. The CoG candidate calculation method is improved for walking and stand-to-sit movements, and an anomaly detection method using CoG candidates is also proposed. The care robot is designed to be user-driven and provide support for persons with insufficient strength based on state estimation. The experiments verify that the developed system can constantly monitor the user’s state and support a series of movements, such as standing up, walking, and sitting down, with a single robot.