Assisting persons with dementia during handwashing using a partially observable Markov decision process

Abstract. This paper presents a real-time system to assist persons with dementia during handwashing. Assistance is given in the form of verbal and/or visual prompts, or through the enlistment of a human caregiver’s help. The system uses only video inputs, and combines a Bayesian sequential estimation framework for tracking hands and towel, with a decision theoretic framework for computing policies of action – specifically a partially observable Markov decision process (POMDP). A key element of the system is the ability to estimate and adapt to user states, such as awareness, responsiveness and overall dementia level. We demonstrate the system in a set of simulation experiments, and we show examples of real-time interactions with actors.