Hyungik Oh

Nitish Nag, Hyungik Oh, Mengfan Tang et al.

It is well understood that an individual's health trajectory is influenced by choices made in each moment, such as from lifestyle or medical decisions. With the advent of modern sensing technologies, individuals have more data and information about themselves than any other time in history. How can we use this data to make the best decisions to keep the health state optimal? We propose a generalized Personal Health Navigation (PHN) framework. PHN takes individuals towards their personal health goals through a system which perpetually digests data streams, estimates current health status, computes the best route through intermediate states utilizing personal models, and guides the best inputs that carry a user towards their goal. In addition to describing the general framework, we test the PHN system in two experiments within the field of cardiology. First, we prospectively test a knowledge-infused cardiovascular PHN system with a pilot clinical trial of 41 users. Second, we build a data-driven personalized model on cardiovascular exercise response variability on a smartwatch data-set of 33,269 real-world users. We conclude with critical challenges in health computing for PHN systems that require deep future investigation.

Hyungik Oh, Ramesh Jain

Events are fundamental for understanding how people experience their lives. It is challenging, however, to automatically record all events in daily life. An understanding of multimedia signals allows recognizing events of daily living and getting their attributes as automatically as possible. In this paper, we consider the problem of recognizing a daily event by employing the commonly used multimedia data obtained from a smartphone and wearable device. We develop an unobtrusive approach to obtain latent semantic information from the data, and therefore an approach for daily event recognition based on semantic context enrichment. We represent the enrichment process through an event knowledge graph that semantically enriches a daily event from a low-level daily activity. To show a concrete example of this enrichment, we perform an experiment with eating activity, which may be one of the most complex events, by using 14 months of data for three users. In this process, to unobtrusively complement the lack of semantic information, we suggest a new food recognition/classification method that focuses only on a physical response to food consumption. Experimental results indicate that our approach is able to show automatic abstraction of life experience. These daily events can then be used to create a personal model that can capture how a person reacts to different stimuli under specific conditions.