Towards precision health by enabling multimodal monitoring in real-life settings using uncertainty based hierarchical and time-dynamic models

I will construct a multimodal and dynamic hierarchical sensing framework to tackle the challenges of personalized health monitoring in real-life settings. Multimodal sensing allows me to detect non-physiological symptoms by incorporating context. By fusing behavior modeling with hierarchical anomaly detection using an active learning approach, I will define the optimal moment to gather user feedback for the time dynamic models.