  This study investigates the use of movement sensor data from a smart watch to
infer an individual's emotional state. We present our findings on a user study
with 50 participants. The experimental design is a mixed-design study;
within-subjects (emotions; happy, sad, neutral) and between-subjects (stimulus
type: audio-visual "movie clips", audio "music clips"). Each participant
experienced both emotions in a single stimulus type. All participants walked
250m while wearing a smart watch on one wrist and a heart rate monitor strap on
their chest. They also had to answer a short questionnaire (20 items; PANAS)
before and after experiencing each emotion. The heart rate monitor served as
supplementary information to our data. We performed time-series analysis on the
data from the smart watch and a t-test on the questionnaire items to measure
the change in emotional state. The heart rate data was analyzed using one-way
ANOVA. We extracted features from the time-series using sliding windows and
used the features to train and validate classifiers that determined an
individual's emotion. Participants reported feeling less negative affect after
watching sad videos or after listening to the sad music, P < .006. For the task
of emotion recognition using classifiers, our results show that the personal
models outperformed personal baselines, and achieved median accuracies higher
than 78% for all conditions of the design study for the binary classification
of happiness vs sadness. Our findings show that we are able to detect the
changes in emotional state with data obtained from the smartwatch as well as
behavioral responses.
