  Emotion prediction is a key emerging research area that focuses on
identifying and forecasting the emotional state of a human from multiple
modalities. Among other data sources, physiological data can serve as an
indicator for emotions with an added advantage that it cannot be
masked/tampered by the individual and can be easily collected. This paper
surveys multiple machine learning methods that deploy smartphone and
physiological data to predict emotions in real-time, using self-reported
ecological momentary assessments (EMA) scores as ground-truth. Comparing
regression, long short-term memory (LSTM) networks, convolutional neural
networks (CNN), reinforcement online learning (ROL), and deep belief networks
(DBN), we showcase the variability of machine learning methods employed to
achieve accurate emotion prediction. We compare the state-of-the-art methods
and highlight that experimental performance is still not very good. The
performance can be improved in future works by considering the following
issues: improving scalability and generalizability, synchronizing multimodal
data, optimizing EMA sampling, integrating adaptability with sequence
prediction, collecting unbiased data, and leveraging sophisticated feature
engineering techniques.
