Monitoring the Mental State of Cooperativeness for Guiding an Elderly Person in Sit-to-Stand Assistance

TL;DR

This paper proposes a framework for monitoring and estimating the mental state of elderly individuals to enhance cooperation during sit-to-stand assistance using a Kalman Filter-based approach.

Contribution

It introduces a modeling and estimation method for assessing elderly cooperation levels, aiding robotic assistance in eldercare.

Findings

Kalman Filter effectively estimates cooperativeness levels.

Thresholding scheme classifies cooperation states accurately.

Framework facilitates adaptive guidance for eldercare robots.

Abstract

In providing physical assistance to elderly people, ensuring cooperative behavior from the elderly persons is a critical requirement. In sit-to-stand assistance, for example, an older adult must lean forward, so that the body mass can shift towards the feet before a caregiver starts lifting the body. An experienced caregiver guides the older adult through verbal communications and physical interactions, so that the older adult may be cooperative throughout the process. This guidance is of paramount importance and is a major challenge in introducing a robotic aid to the eldercare environment. The wide-scope goal of the current work is to develop an intelligent eldercare robot that can a) monitor the mental state of an older adult, and b) guide the older adult through an assisting procedure so that he/she can be cooperative in being assisted. The current work presents a basic modeling framework for describing a human's physical behaviors reflecting an internal mental state, and an algorithm for estimating the mental state through interactive observations. The sit-to-stand assistance problem is considered for the initial study. A simple Kalman Filter is constructed for estimating the level of cooperativeness in response to applied cues, with a thresholding scheme being used to make judgments on the cooperativeness state.