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