PhD, Media Arts and Sciences, MIT, 1999
MS, Media Arts and Sciences, MIT, 1994
BSE, Computer Science and Engineering, University of Pennsylvania, 1992
Personal health informatics, mobile health, ubiquitous computing, behavioral science, human-computer interaction
Prof. Intille’s research focuses on the development and evaluation of personal, behavioral health informatics – how sensor data acquired throughout everyday life from smartwatches, smartphones, wearable monitors, and in-home sensors might be used to improve wellness via novel human-computer interfaces. This research involves merging ideas from the computer science subfields of human-computer interaction and applied machine learning and artificial intelligence with ideas from behavioral science, social psychology, and preventive medicine.
He is particularly interested in how algorithms that reliably recognize everyday activities and habits can drive the development of interactive preventive health tools that could be deployed at population scale in a cost-effect manner. Within computer science, this requires developing new user-driven activity detection algorithms that use context and common-sense information, without requiring large training sets; a focus is on person-in-the-loop interactive, explanatory behavior recognition interfaces. Within preventive medicine, this requires building and deploying pilot systems and demonstrating that the technology has a meaningful impact on health outcomes; a focus is on demonstrating that technology can support long-term engagement with behavior change maintenance, especially to support physical activity.
Overall, Prof. Intille’s research group develops and evaluates novel sensor-based tools that can be used to both measure and motivate behavior change. He has published research on computational stereo depth recovery, real-time and multi-agent tracking, activity recognition, perceptually-based interactive environments, and technology for healthcare. He has been principal investigator on sensor-enabled health technology grants from the NSF, the NIH, foundations, and industry. He directs Northeastern’s Ph.D. program in Personal Health Informatics.
IEEE Pervasive Computing Associate Editor in Chief and Editorial Board
PACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) Associate Editor
G. F. Dunton, A. J. Rothman, A. M. Leventhal, and S.S. Intille, “How intensive longitudinal data can stimulate advances in health behavior maintenance theories and interventions,” Translational Behavioral Medicine, vol. 11, pp. 281-286, 2021.
D. John, Q. Tang, F. Albinali, and S. Intille, “An open-source monitor-independent movement summary for accelerometer data processing,” Journal for the Measurement of Physical Behaviour, vol. 2, pp. 268-281, 2019.
P. H. Lin, S. Grambow, S. Intille, J. Gallis, T. Lazenka, H. Bosworth, C. Voils, G. Bennett, B. Batch, J. Allen, L. Corsino, C. Tyson, and L. Svetkey, “The association between engagement and weight loss through personal coaching and cell phone interventions in young adults: Randomized controlled trial,” JMIR mHealth and uHealth, vol. 6, p. e10471, 2018.
Ponnada, C. Haynes, D. Maniar, J. Manjourides, and S. Intille, “Microinteraction ecological momentary assessment response rates: Effect of microinteractions or the smartwatch?,” Proc. of the ACM Journal on Interactive, Mobile, Wearable, and Ubiquitous Technology, vol. 1, 2017.
Spruijt-Metz, E. Hekler, N. Saranummi, S. Intille, I. Korhonen, W. Nilsen, D. Rivera, B. Spring, S. Michie, D. Asch, A. Sanna, V. Salcedo, R. Kukakfa, and M. Pavel, “Building new computational models to support health behavior change and maintenance: New opportunities in behavioral research,” Translational Behavioral Medicine, vol. 5, pp. 335-46, 2015.
DS 2001 Programming with Data – Health Practicum
HONR 3310 Creating the Future: Transforming Healthcare with Mobile Health
HINF 5300 Personal Health Interfaces Design and Development
HSCI 4740 Section 2 Health Science Capstone Seminar (Active Transportation via Cycling)
PHTH 5228 Advances In Measuring Behavior
IS 4300/CS 5340 Human-Computer Interaction
IS 5800 Empirical Research Methods