Modeling User Context from Smartphone Data for Recognition of Health Status

Recent advances in sensing technology have made it possible to monitor how behavioral systems unfold in people's natural settings by leveraging sensors embedded in personal smartphones and other smart devices. This paper provides a framework for how smartphone sensor data can be collected, cleaned, and modeled to predict relevant disease contexts such as location. These variables can then be used in context-sensitive models to understand how a user's behavior and contexts might differ from typical patterns when impacted by illness. To develop rich contextual models, we first conducted a 2-week smartphone monitoring study where sensor data and corresponding location contexts were tagged for 7 users. Next, we demonstrated how multimodal sensor data can be used to predict location context by modeling the tagged dataset and analyzing differences in sensors to find indicators for each location. The results of this effort include 1) identification of ground truth data for contexts of interest to be used in future modeling, 2) establishment of a process to collect, clean, and visualize smartphone data generated by both iOS and Android systems, and 3) creation of models to predict a participant's location and context using raw smartphone data. This context identification process could be used in future research to perform analyses that leverage past patterns of user behavior to recognize disease indicators.