Automated Activity Interventions to Assist with Activities of Daily Living

Over the last decade there has been a significant growth of research endeavors in the area of ambient intelligence or smart environments. An anticipated increase in the older adult population around the globe and an increase in health care expenditures as a result, has increased the demand of smart health assistance systems. Along with the classical problems of remote health monitoring and activity tracking, delivering in-home interventions to residents for timely reminders or brief instructions to ensure successful completion of daily activities, is receiving a significant amount of attention in the community. In this chapter, the problem of delivering in-home interventions has been described in detail and some of the prospective approaches have been compared and contrasted. The approaches, details and challenges mentioned in this chapter revolve around 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 machine learning problem faced while learning the timing of prompts is that the class of training instances that represent prompt situations is under represented 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.