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Just-in-Time Adaptive Interventions (JITAIs) are a class of personalized health interventions developed within the behavioral science community. JITAIs aim to provide the right type and amount of support by iteratively selecting a sequence of intervention options from a pre-defined set of components in response to each individual's time varying state. In this work, we explore the application of reinforcement learning methods to the problem of learning intervention option selection policies. We study the effect of context inference error and partial observability on the ability to learn effective policies. Our results show that the propagation of uncertainty from context inferences is critical to improving intervention efficacy as context uncertainty increases, while policy gradient algorithms can provide remarkable robustness to partially observed behavioral state information. 

Stream-based active learning methods assume that data instances arrive in sequence and the decision must be made to query an instance or not as it arrives. In mobile health and human activity recognition, the data stream is often block-structured where instances in the same block have the same label, but the boundaries between blocks are unobserved. In this paper, we propose an approach to active learning in this setting where we simultaneously learn to segment the stream while learning an instance-level discriminative classifier. We show that by propagating collected labels into inferred segments, we can learn improved predictive models with significantly fewer queries. 

In mobile health (mHealth) and human activity recognition (HAR), collecting labeled data often comes at a significantly higher cost or level of user burden than collecting unlabeled data. This motivates the idea of attempting to optimize the collection of labeled data to minimize cost or burden. In this paper, we develop active learning methods that are tailored to the mHealth and HAR domains to address the problems of labeled data scarcity and the cost of labeled data collection. Specifically, we leverage between-user similarity to propose a novel hierarchical active learning framework that personalizes models for each user while sharing the labeled data collection burden across a group, thereby reducing the labeling effort required by any individual user. We evaluate our framework on a publicly available human activity recognition dataset. Our hierarchical active learning framework on average achieves between a 20% and 70% reduction in labeling effort when compared to standard active learning methods.