Motivated by the rising caregiver burden and challenges in remote health behavior monitoring, the proposed research will enable effective assistive interventions in response to dynamically changing health behaviors for target populations. To be effective and impactful, assistive mechanisms need to capture and respond to the subtle and changing context of the human. Human behaviors, however, are challenging to learn due to their complexity and the constantly changing physical, social, and environmental context. Recently, wearables have emerged to fill this gap as users are adopting a variety of devices to help them monitor health related parameters. Given their ubiquity, wearables are positioned ideally to deliver persuasive content aimed at improving users? health outcomes. However, there is a need for a holistic approach to infer human health behaviors, even as the user's context and the devices measuring their behavior vary over time. The proposed research has the potential to transform human health outcomes by capturing and responding to fine-grained behavioral information continuously, inexpensively, and unobtrusively. This human-in-the-loop system will facilitate rapid development of Health applications by providing the foundations for using adaptive and personalized interventions for diverse health populations to enable assistive care for all.