Aristotle Said “Happiness is a State of Activity” — Predicting Mood Through Body Sensing with Smartwatches

  • Peter A. GloorEmail author
  • Andrea Fronzetti Colladon
  • Francesca Grippa
  • Pascal Budner
  • Joscha Eirich


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.


Body sensing systems mood tracking smartwatch experience sampling happiness activation 


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The authors are grateful to the referees for their constructive input. The authors would like to thank Cihan Öcal, Jan Marc Misselich, Ines Rieger, Anne Schleicher, Xia Yu, Liqi, and Gavin who, as participants in a collaborative innovation networks seminar at University of Cologne, University of Bamberg, and Jilin University, Changchun, were part of the team building the basic smartwatch software infrastructure. We would also like to thank the team at Swissnex Boston, Dan Aufseesser, Francesco Bortoluzzi, Jonas Brunschwig, Felix Moesner, Sophie Sithamma, Anita Suter, Cécile Vulliemin, Gary Weckx for being early testers of our system. This project has been supported by a series of grants by Philips Lighting (now Signify).


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Copyright information

© Systems Engineering Society of China and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Peter A. Gloor
    • 1
    Email author
  • Andrea Fronzetti Colladon
    • 2
  • Francesca Grippa
    • 3
  • Pascal Budner
    • 4
  • Joscha Eirich
    • 5
  1. 1.MIT Center for Collective IntelligenceCambridgeUSA
  2. 2.Department of Enterprise EngineeringUniversity of Rome Tor VergataRomeItaly
  3. 3.Northeastern UniversityBostonUSA
  4. 4.University of Cologne, Albertus-Magnus-PlatzKölnGermany
  5. 5.University of BambergBambergGermany

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