Towards Learning Efficient Intervention Policies for Wearable Devices

Matthew Saponaro, Haoran Wei, Keith Decker

Abstract: To serve large user populations, autonomous intervention systems (i.e. intelligent agents) are being developed to play more active roles such as fitness coaches and clinical disease prevention aids. Although generic user models have been developed, users may require extensive individualization to meet their personal needs. Machine learning techniques may be applied to learn tailored intervention policies for users. However, traditional machine learning requires significant amounts of data to learn an optimal policy. For wearable technology, this may mean probing the user to perform some activity and gauging user response. This paper presents a feasible intervention system model and discusses learners for tailoring user intervention policies. We examine how similar the general user model has to be with respect to the tailored model in order for our learner to perform well.