Dementia affects more than 35 million people worldwide, and the number is still growing dramatically. Dementia affects people’s ability to live independently, thus many dementia patients face the prospect of being institutionalized. As home care is often costly, we are investigating the development of an affordable socially assistive robot to support cognitively impaired older adults with daily living activities, which will potentially extend their time living at home.
To communicate with patients effectively, a caregiving robot should tailor its interactions to its interlocutors, taking into account the nature and degree of cognitive impairment, other health issues, and individual preferences.
We equipped the robot with a knowledge base embodying “best practices” for communicating with different classes of individuals [practice1,practice2, rochon, web1, web2, web3]. Upon delivery, the knowledge base can be augmented with (some) information about the people with whom the robot is expected to interact. When communicating with an individual, the robot uses the knowledge base to determine a customized strategy.
People with dementia often have difficulty completing daily tasks. In this work, we customized the interactions of the robot with users of the COACH system, which provides assistance to complete the task of handwashing~[COACH]. COACH tracks the user’s hand positions to ensure that he or she is following a proper sequence to wash their hands. If COACH determines that the user is having difficulty, it will provide audio or video instructions, called $prompts$. These prompts are then customized by our system.
COACH uses a POMDP-based $Task Manager$ to estimate the user’s progress and determine the next prompt. The prompt is then processed by the $Executor$ which plays a pre-recorded audio or video clip (see Fig. 1).
To customize the behaviour of COACH, we deployed a $Prompter$ which reasons using features stored in the knowledge base to adapt the prompt for the user. We also modified the Executor so that it could use the output of the Prompter to dynamically generate an appropriate audio clip, rather than simply using a pre-recorded one (see Fig. 2).
Figure 1: The original COACH system.
Figure 2: COACH system with customization
Figure 3: The architecture of the Prompter
We would like to thank our collaborators: CrossWing Inc.; Professor Alex Mihailidis and team at the Toronto Rehabilitation Institute; Professor Goldie Nejat and team in Mechanical and Industrial Engineering at the University of Toronto; and Professor François Michaud and team at the University of Sherbrooke. We also wish to thank Dr.Elizabeth Rochon from the Toronto Rehabilitation Institute for her expert advice on suitable strategies for communicating with persons with dementia. Computations were performed on the SOSCIP Consortium’s Cloud Data Analytics computing platform. SOSCIP is funded by the Federal Economic Development Agency of Southern Ontario, the Province of Ontario, IBM Canada Ltd., Ontario Centres of Excellence, Mitacs and 15 Ontario academic member institutions.