The growth in popularity of smart environments has been quite steep in the last decade and so has the demand for smart health assistance systems. A smart home-based prompting system can enhance these technologies to deliver in-home interventions to users for timely reminders or brief instructions describing the way a task should be done for successful completion. This technology is in high demand given the desire of people who have physical or cognitive limitations to live independently in their homes. In this paper, with the introduction of the "PUCK" prompting system, we take an approach to automate prompting-based interventions without any predefined rule sets or user feedback. Unlike other approaches, we use simple off-the-shelf sensors and learn the timing for prompts based on real data that is collected with volunteer participants in our smart home test bed. The data mining approaches taken to solve this problem come with the challenge of an imbalanced class distribution that occurs naturally in the data. We propose a variant of an existing sampling technique, SMOTE, to deal with the class imbalance problem. To validate the approach, a comparative analysis with Cost Sensitive Learning is performed.
Keywords: Automated prompting; Imbalanced class distribution; Machine learning; Prompting systems; Smart environments.