Decoding Emotional Valence from Wearables: Can Our Data Reveal Our True Feelings?
This research addresses the challenge of transferring emotion detection from lab to real-life for potential mental health applications, but it is incremental with a small preliminary study.
The study tackled the problem of detecting emotional valence in real-world settings using consumer-grade wearables, achieving an F1 score of 0.65 for distinguishing between high and low positive valence.
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.