Consumer Wearables and Affective Computing for Wellbeing Support

Saganowski, Stanisław; Kazienko, Przemysław; Dzieżyc, Maciej; Jakimów, Patrycja; Komoszyńska, Joanna; Michalska, Weronika; Dutkowiak, Anna; Polak, Adam G.; Dziadek, Adam; Ujma, Michał

doi:10.1145/3448891.3450332

Cited by 24 publications

(14 citation statements)

References 29 publications

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“…However, one should remember that there are still many limitations standing in the wy of the wider use of wearable devices in AAER. First of all, the quality of physiological signals is still noticeably lower than that of medical-level equipment [ 98 ]. What is more, the data gathered by such instruments in non-laboratory settings are often flawed, with noise coming from motion or misplacement [ 99 ].…”

Section: Discussionmentioning

confidence: 99%

Datasets for Automated Affect and Emotion Recognition from Cardiovascular Signals Using Artificial Intelligence— A Systematic Review

Jemioło

Storman

Mamica

et al. 2022

Sensors

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Our review aimed to assess the current state and quality of publicly available datasets used for automated affect and emotion recognition (AAER) with artificial intelligence (AI), and emphasising cardiovascular (CV) signals. The quality of such datasets is essential to create replicable systems for future work to grow. We investigated nine sources up to 31 August 2020, using a developed search strategy, including studies considering the use of AI in AAER based on CV signals. Two independent reviewers performed the screening of identified records, full-text assessment, data extraction, and credibility. All discrepancies were resolved by discussion. We descriptively synthesised the results and assessed their credibility. The protocol was registered on the Open Science Framework (OSF) platform. Eighteen records out of 195 were selected from 4649 records, focusing on datasets containing CV signals for AAER. Included papers analysed and shared data of 812 participants aged 17 to 47. Electrocardiography was the most explored signal (83.33% of datasets). Authors utilised video stimulation most frequently (52.38% of experiments). Despite these results, much information was not reported by researchers. The quality of the analysed papers was mainly low. Researchers in the field should concentrate more on methodology.

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Section: Discussionmentioning

confidence: 99%

Datasets for Automated Affect and Emotion Recognition from Cardiovascular Signals Using Artificial Intelligence— A Systematic Review

Jemioło

Storman

Mamica

et al. 2022

Sensors

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“…The usage of wearable technologies for research has nearly doubled in the last few years [15]. Due to their unobtrusiveness and convenience, wearables are increasingly being utilized by individuals to improve their well-being, sleep, and fitness [10], [16]. For instance, recently, wearables have allowed researchers to effectively detect seizures [17], [18] and help with the precision management of diabetes [19].…”

Section: Introductionmentioning

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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“…behavioral and physiological components of emotions [1], [7]. Only a few studies were trying to recognize real-life emotions, i.e., research in the field, especially [2], [8], [9] lasting a dozen days, on pupils in the classroom [10], on workers in the factory [11], or [12]- [14] focusing primarily on mood.…”

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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Consumer Wearables and Affective Computing for Wellbeing Support

Cited by 24 publications

References 29 publications

Datasets for Automated Affect and Emotion Recognition from Cardiovascular Signals Using Artificial Intelligence— A Systematic Review

Datasets for Automated Affect and Emotion Recognition from Cardiovascular Signals Using Artificial Intelligence— A Systematic Review

Ethical Considerations and Checklist for Affective Research With Wearables

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

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