  The possibility of recognizing diverse aspects of human behavior and
environmental context from passively captured data motivates its use for mental
health assessment. In this paper, we analyze the contribution of different
passively collected sensor data types (WiFi, GPS, Social interaction, Phone
Log, Physical Activity, Audio, and Academic features) to predict daily
selfreport stress and PHQ-9 depression score. First, we compute 125 mid-level
features from the original raw data. These 125 features include groups of
features from the different sensor data types. Then, we evaluate the
contribution of each feature type by comparing the performance of Neural
Network models trained with all features against Neural Network models trained
with specific feature groups. Our results show that WiFi features (which encode
mobility patterns) and Phone Log features (which encode information correlated
with sleep patterns), provide significative information for stress and
depression prediction.
