SN̈AP: SyNdetic Assistance Processes

Activity recognition in intelligent environments could play a key role for supporting people in their activities of daily life. Partially observable Markov decision process (POMDP) models have been used successfully, for example, to assist people with dementia when carrying out small multi-step tasks such as hand washing. POMDP models are a powerful, yet flexible framework for modeling assistance that can deal with uncertainty and utility in a theoretically well-justified manner. Unfortunately, POMDPs usually require a very labor intensive, manual setup procedure. This paper describes a knowledge driven method for automatically generating POMDP activity recognition and context sensitive prompting systems for a complex tasks. We call the resulting POMDP a SN̈AP (SyNdetic Assistance Process). The method starts with a psychologically justified (syndetic) description of the task and the particular environment in which it is to be carried out that can be generated from empirical data. This is then combined with a specification of the available sensors and effectors to build a working prompting system that tracks a person’s activities and learns their abilities by using sensor data as evidence in the context of the SN̈AP POMDP. The method is illustrated by building a system that prompts through the task of making a cup of tea in a real-world kitchen.