How to catch them all? Enhanced data collection for emotion recognition in the field

Dzieżyc, Maciej; Komoszyńska, Joanna; Saganowski, Stanisław; Boruch, Magda; Dziwinski, Jakub; Jablonska, K.; Kunc, Dominika; Kazienko, Przemysław

doi:10.1109/percomworkshops51409.2021.9431143

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…(3) language and study instructions not appropriate to the intellectual and technological proficiency of the participants; (4) anticipating missing data; (5) overall data anonymization and security; (6) balancing the burden on study participants with the benefit to researchers, e.g., asking too many questions or too often; (7) technical limitations of devices, e.g., sampling rate, low battery; (8) choosing the inappropriate emotion model (e.g., outdated or not suitable for the later needs of creating a machine learning models [12]) ; (9) inference model use; (10) amount and method of compensation; (11) data quality; or (12) overgeneralization of context while experiencing emotions.…”

Section: General Risksmentioning

confidence: 99%

Ethical Considerations and Checklist for Affective Research With Wearables

Behnke

Saganowski

Kunc

et al. 2024

IEEE Trans. Affective Comput.

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As the popularity of wearables increases, so does their utility for studying emotions. Using new technologies points to several ethical challenges to be considered to improve research designs. There are several ethical recommendations for utilizing wearables to study human emotions, but they focus on emotion recognition systems applications rather than research design and implementation. To address this gap, we have developed a perspective on wearables, especially in daily life, adapting the ReCODE Health -Digital Health Framework and companion checklist. Therefore, our framework consists of four domains: (1) participation experience, (2) privacy, (3) data management, and (4) access and usability. We identified 33 primary risks of using wearables to study emotions, including research-related negative emotions, collecting, processing, storing, sharing personal and biological information, commercial technology validity and reliability, and exclusivity issues. We also proposed possible strategies for minimizing risks. We consulted the new ethical guidelines with members of ethics committees and relevant researchers. The judges (N = 26) positively rated our solutions and provided useful feedback that helped us refine our guidance. Finally, we summarized our proposals with a checklist for researchers' convenience. Our guidelines contribute to future research by providing improved protection of participants' and scientists' interests.

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Section: General Risksmentioning

confidence: 99%

Ethical Considerations and Checklist for Affective Research With Wearables

Behnke

Saganowski

Kunc

et al. 2024

IEEE Trans. Affective Comput.

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“…The primary goal of Study A was to collect physiological signals during emotionally intense moments in participants' everyday lives. The collected emotionally annotated signals were then used for creating an ML model recognizing intense emotions in real-time [21]. The model was further used for more efficient data gathering in Study A and Study B.…”

Section: B Datamentioning

confidence: 99%

“…The ESM provides high ecological validity of the repeated in-the-moment experience measurement, in which participants receive the measurements' notifications in a semirandom design. However, ESM can be further improved with the recent developments in affective computing, in which the measurement moments can be detected by physiologically or behaviorally driven pre-trained machine learning (ML) models [15], [21].…”

Section: Introduction and Related Workmentioning

confidence: 99%

The Cold Start Problem and Per-Group Personalization in Real-Life Emotion Recognition With Wearables

Saganowski

Kunc

Perz

et al. 2022

2022 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Work

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Emotion recognition in real life from physiological signals provided by wrist worn devices still remains a great challenge especially due to difficulties with gathering annotated emotional events. For that purpose, we suggest building pretrained machine learning models capable of detecting intense emotional states. This work aims to explore the cold start problem, where no data from the target subjects (users) are available at the beginning of the experiment to train the reasoning model. To address this issue, we investigate the potential of pergroup personalization and the amount of data needed to perform it. Our results on real-life data indicate that even a week's worth of personalized data improves the model performance.

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