Deciding the different robot roles for patient cognitive training

Abstract

Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI) represent a major challenge for health systems within the aging population. New and better instruments will be crucial to assess the disease severity and progression, as well as to improve its treatment, stimulation, and rehabilitation. With the purpose of detecting, assessing and quantifying cognitive impairments like MCI or AD, several methods are employed by clinical experts. Syndrom Kurztest neuropsychological battery (SKT) is a simple and short test to measure cognitive decline as it assesses memory, attention, and related cognitive functions, taking into account the speed of information processing. In this paper, we present a decision system to embed in a robot that can set up a productive interaction with a patient, and can be employed by the caregiver to motivate and support him while performing cognitive exercises as SKT. We propose two different interaction loops. First, the robot interacts with the caregiver in order to set up the mental and physical impairments of the patient and indicate a goal of the exercise. This is used to determine the desired robot behavior (human-centric or robot-centric, and preferred interaction modalities). Second, the robot interacts with the patient and adapts its actions to engage and assist him to complete the exercise. Two batches of experiments were conducted, and the results indicated that the robot can take profit of the initial interaction with the caregiver to provide a quicker personalization, and also it can adapt to different user responses and provide support and assistance at different levels of interaction.