  We measure and predict states of Activation and Happiness using a body
sensing application connected to smartwatches. Through the sensors of
commercially available smartwatches we collect individual mood states and
correlate them with body sensing data such as acceleration, heart rate, light
level data, and location, through the GPS sensor built into the smartphone
connected to the smartwatch. We polled users on the smartwatch for seven weeks
four times per day asking for their mood state. We found that both Happiness
and Activation are negatively correlated with heart beats and with the levels
of light. People tend to be happier when they are moving more intensely and are
feeling less activated during weekends. We also found that people with a lower
Conscientiousness and Neuroticism and higher Agreeableness tend to be happy
more frequently. In addition, more Activation can be predicted by lower
Openness to experience and higher Agreeableness and Conscientiousness. Lastly,
we find that tracking people's geographical coordinates might play an important
role in predicting Happiness and Activation. The methodology we propose is a
first step towards building an automated mood tracking system, to be used for
better teamwork and in combination with social network analysis studies.
