Over the last decade there has been a significant growth of research endeavors in the area of ambient intelligence. An anticipated increase in the older adult population around the world and increasing health care expenditures have increased the demand of smart health assistance systems. Delivering in-home activity interventions to residents for timely reminders ensuring successful completion of daily activities, is receiving significant attention in the community. In this chapter, the problem of automated in-home activity interventions is described and prospective solutions are compared. The approaches and challenges are based on a prototypic model of an automated prompting system, namely PUCK, which is an on-going project at the Center for Advanced Studies in Adaptive Systems at Washington State University. The previous study done on this project investigated the application of machine learning techniques to identify appropriate timing of prompts based on data provided by off-the-shelf sensors. The fundamental problem in learning timing of prompts is caused due to the under representation of prompt situations in the training examples as compared to no-prompt situations. While a method was originally proposed to deal with this problem, popularly known as learning from imbalanced class distributions, in this chapter a novel Cluster-Based Under-sampling (CBU) approach is proposed that shows promising results.