Traditionally, mood is assessed by either considering physiological data or smartphones-based self-reports. Physiological data is objective and continuous, but difficult to be collected in-field and lacks a subjective component. Smartphones provide subjective feedback and objective data, but lack physiological data. We propose to combine smartphones as a rich sensor system and smartwatches as a wearable heart rate monitor. Both serve as a platform for reporting mood states. Within an explorative user study with six subjects over four weeks, we collected smartphone data and heart rate in addition to subjective ground truth via self-reports. We assessed all three mood dimensions  valence, energetic arousal  and  calmness , but only consider  valence  in the context of this paper. Analyzing the information gain, we identified the relevance of temporal features (daytime, weekday, type of day) as well as the heart rate. Decision tree classifiers trained on the first three weeks and tested on the fourth achieve recognition accuracies of up to 0.91.
['Anja Exler', 'Andrea Schankin', 'Christoph Klebsattel', 'Michael Beigl']
A wearable system for mood assessment considering smartphone features and data from mobile ECGs