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We measure and predict states of Activation and Happiness using a bodysensing application connected to smartwatches. Through the sensors ofcommercially available smartwatches we collect individual mood states andcorrelate them with body sensing data such as acceleration, heart rate, lightlevel data, and location, through the GPS sensor built into the smartphoneconnected to the smartwatch. We polled users on the smartwatch for seven weeksfour times per day asking for their mood state. We found that both Happinessand Activation are negatively correlated with heart beats and with the levelsof light. People tend to be happier when they are moving more intensely and arefeeling less activated during weekends. We also found that people with a lowerConscientiousness and Neuroticism and higher Agreeableness tend to be happymore frequently. In addition, more Activation can be predicted by lowerOpenness to experience and higher Agreeableness and Conscientiousness. Lastly,we find that tracking people's geographical coordinates might play an importantrole in predicting Happiness and Activation. The methodology we propose is afirst step towards building an automated mood tracking system, to be used forbetter teamwork and in combination with social network analysis studies.

Aristotle Said "Happiness is a State of Activity" -- Predicting Mood through Body Sensing with Smartwatches