Decoding Emotional Valence from Wearables: Can Our Data Reveal Our True Feelings?
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914637259210752 |
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| author | Grzeszczyk, Michal K. Lisowska, Anna Sitek, Arkadiusz Lisowska, Aneta |
| author_facet | Grzeszczyk, Michal K. Lisowska, Anna Sitek, Arkadiusz Lisowska, Aneta |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05408 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Decoding Emotional Valence from Wearables: Can Our Data Reveal Our True Feelings? Grzeszczyk, Michal K. Lisowska, Anna Sitek, Arkadiusz Lisowska, Aneta Signal Processing Machine Learning 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. |
| title | Decoding Emotional Valence from Wearables: Can Our Data Reveal Our True Feelings? |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2401.05408 |