There are major hurdles to using mobile sensor data to advance research on computational modeling of human health and behavior, including lack of access to high-quality mobile sensor data, regulatory obligations in accessing and using mobile sensor data collected from humans, and a lack of metadata capture and access services for the provenance, quality, and integrity of the data and inferences made from it. CP7 is developing a new cyberinfrastructure called mProv to annotate high-frequency mobile sensor data with data source, quality, validity, and semantics to facilitate the sharing of such data with the wider research community for third party research. It is developing techniques to integrate metadata and data capture over mobile streaming data, and propagate such data in order to enable reasoning about uncertainty and variability; runtime infrastructure and APIs for efficient sensor data acquisition and reply (integrated with human data capture), and mechanisms for managing privacy policies. To support interpretation of sensor-derived features and inferences (i.e., markers of health, behavior, and context) by researchers (for concurrent development that makes use of datastreams developed by other researchers) and automating analysis by machines, CP7 has developed datastream representation to support a common metadata structure that allows both mCerebrum and Cerebral Cortex (installed on mobile phones and the cloud respectively) to annotate the datastream with metadata. It has also developed storage, interface, instrumentation, and visualization tools for provenance tracking through stream processing operators. Provenance information can be automatically captured as a series of entities, activities, and relationships in a graph database from which it can be queried or visualized, even in near-real-time. It has built a core provenance repository with user authentication, group creation, and metadata storage capabilities. These are exposed through a simple REST microservices framework, and they can be retargeted at the back-end to a variety of SQL and NoSQL database systems. Currently, CP7 software uses Cassandra, REDIS, Neo4J, and Postgres. CP7 also works closely with an R24 from NIBIB (R24EB025845; PI: Ida Sim, UC San Francisco; 7/1/17-6/30/20) to standardize biomarkers that have been validated and being adopted in the research community via an IEEE Working Group (P1752, Open Mobile Health Standards). To evaluate its work under realistic settings, CP7 is conducting multiple iterations of 100-day field studies in 100 participants in collaboration with the Open Humans project to generate open data set that can be used by researchers to develop mHealth biomarkers for detecting daily stressors.
CP, Emotional Context, Stress, TR&D3