Sohrob Saeb received his PhD in Computer Science (Computational Neuroscience) from Goethe-Universität Frankfurt where his research focused on modeling the neurocomputational mechanisms of coordinated eye and head movements in primate brain. His current research at CBITs focuses on context sensing in mental health, which is about the use of sensor data collected from smartphones and wearable devices to infer information about different aspects of patients’ daily lives and their environments
The aim of this project is to develop machine learning algorithms that can passively infer the amount of face-to-face communication in depressed patients by analyzing the audio data collected from their smartphones, and thereby provide a momentary, objective assessment of the sociability factor in the context of their daily lives.
Current mobile phone and wearable sensor technologies allow us to track a person's physical activities throughout their daily lives. In many circumstances, however, there are strong incentives for users to trick the activity recognition systems into detecting activities that are different from the actual ones.
Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study
Saeb, S., Zhang, M., Karr, C.J., Schueller, S.M., Corden, M., Kording, K.P., Mohr, D.C.
Journal of Medical Internet Research. 2015.
The relationship between clinical, momentary, and sensor-based assessment of depression
Saeb, S, Zhang S, Karr CJ, Corden ME, Kwasny M, Mohr DC
Proc. Pervasive Health 2015, Istanbul, Turkey, May 20-23, 2015 (In Press).
Above are selected publications
For more of Dr. Saeb's publications, click here.