  Automatic detection and tracking of emotional states has the potential for
helping individuals with various mental health conditions. While previous
studies have captured physiological signals using wearable devices in
laboratory settings, providing valuable insights into the relationship between
physiological responses and mental states, the transfer of these findings to
real-life scenarios is still in its nascent stages. Our research aims to bridge
the gap between laboratory-based studies and real-life settings by leveraging
consumer-grade wearables and self-report measures. We conducted a preliminary
study involving 15 healthy participants to assess the efficacy of wearables in
capturing user valence in real-world settings. In this paper, we present the
initial analysis of the collected data, focusing primarily on the results of
valence classification. Our findings demonstrate promising results in
distinguishing between high and low positive valence, achieving an F1 score of
0.65. This research opens up avenues for future research in the field of mobile
mental health interventions.
