Incorporating engineered features of day-to-day, in-person and online social exposure from IoT data to improve health sensing predictive performance.
Status of this project: on the back burner, but collaborators welcome.
- Wu, C., Barczyk, A. N., Craddock, R. C., Harari, G. M., Thomaz, E., Shumake, J. D., … & Schnyer, D. M. (2021). Improving prediction of real-time loneliness and companionship type using geosocial features of personal smartphone data. Smart Health, 20, 100180.
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- Wu, C., Cai, L., Gerber, M. S., Boukhechba, M., & Barnes, L. E. (2018, October). Vector space representation of bluetooth encounters for mental health inference. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (pp. 1691-1699).
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