  Monitoring and understanding affective states are important aspects of
healthy functioning and treatment of mood-based disorders. Recent advancements
of ubiquitous wearable technologies have increased the reliability of such
tools in detecting and accurately estimating mental states (e.g., mood, stress,
etc.), offering comprehensive and continuous monitoring of individuals over
time. Previous attempts to model an individual's mental state were limited to
subjective approaches or the inclusion of only a few modalities (i.e., phone,
watch). Thus, the goal of our study was to investigate the capacity to more
accurately predict affect through a fully automatic and objective approach
using multiple commercial devices. Longitudinal physiological data and daily
assessments of emotions were collected from a sample of college students using
smart wearables and phones for over a year. Results showed that our model was
able to predict next-day affect with accuracy comparable to state of the art
methods.
