Bio-inspired Cognitive Decision-making to Personalize the Interaction and the Selection of Exercises of Social Assistive Robots in Elderly Care

Abstract

Socially assistive robots in healthcare have reported positive results in recent years, for example, in reducing the impact of mild cognitive impairment in older adults. The lack of a qualified workforce and the increase in the older adult population in developed countries have encouraged designers to develop socially assistive robots that operate autonomously by bringing in cognitive and decision-making methods to facilitate the caregivers’ tasks, select the most appropriate activities, and personalize the interaction. This paper presents the development of a cognitive human-inspired decision-making system for autonomous social assistive robots managing the personalized selection of exercises in cognitive stimulation and providing affective support to their users. The decision-making system receives inputs from the robot’s perceptions, user information stored in the robot’s memory, events in an agenda, and information from a bio-inspired module. These inputs generate autonomous decisions that drive the robot’s behavior depending on each situation. We show the system’s capacity, integrated into our Mini social robot, to adapt the interaction, select tailored exercises based on the user’s features, and execute exercises previously programmed by a caregiver to alleviate cognitive deterioration and accompany older people. Besides, the system generates a natural robot behavior based on biologically inspired methods to personalize activities, engage the user, and increase the number of robot services.