Aristotle Said "Happiness is a State of Activity" -- Predicting Mood through Body Sensing with Smartwatches
This work addresses mood prediction for individuals using wearable technology, but it is incremental as it builds on existing sensing methods without introducing a new paradigm.
The study tackled predicting mood states (Happiness and Activation) by correlating them with body sensing data from smartwatches, finding negative correlations with heart rate and light levels, and identifying personality traits and movement as predictors.
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.