This project aims to advance knowledge of machine learning for human-in-the-loop cyber-physical systems. Mobile and wearable devices have emerged as a promising technology for health monitoring and behavioral interventions. Designing such systems involves collecting and labeling sensor data in free-living environments through an active learning process. In active learning, the system iteratively queries a human expert (e.g., patient, clinician) for correct labels. Designing active learning strategies in uncontrolled settings is challenging because (1) active learning places a significant burden on the user and compromises adoption of the technology; and (2) labels expressed by humans carry significant amounts of temporal and spatial disparities that lead to poor performance of the system. The research will address technical challenges in designing high performance systems, and enable accurate monitoring and interventions in many applications beyond behavioral medicine.

This project develops mixed-initiative solutions that will enable learning of human behaviors in uncontrolled environments through the following research objectives: (1) investigating combinatorial approaches to maximize the active learning performance taking into account informativeness of sensor data, burden of data labeling, and reliability of prospective labels; (2) constructing a rich vocabulary of complex behaviors based on knowledge graph embedding and semi-supervised learning techniques; (3) developing network-graph-based learning algorithms that infer complex human behaviors; and (4) validating algorithms for off-line active learning, real-time active learning, behavior vocabulary construction, and behavior inference through both in-lab experiments and user studies.