  This study investigates the use of accelerometer data from a smart watch to
infer an individual's emotional state. We present our preliminary findings on a
user study with 50 participants. Participants were primed either with
audio-visual (movie clips) or audio (classical music) to elicit emotional
responses. Participants then walked while wearing a smart watch on one wrist
and a heart rate strap on their chest. Our hypothesis is that the accelerometer
signal will exhibit different patterns for participants in response to
different emotion priming. We divided the accelerometer data using sliding
windows, extracted features from each window, and used the features to train
supervised machine learning algorithms to infer an individual's emotion from
their walking pattern. Our discussion includes a description of the
methodology, data collected, and early results.
