A system for collecting emotionally annotated physiological signals in daily life using wearables

Saganowski, Stanisław; Behnke, Maciej; Komoszyńska, Joanna; Kunc, Dominika; Perz, Bartosz; Kazienko, Przemysław

doi:10.1109/aciiw52867.2021.9666272

Cited by 11 publications

(6 citation statements)

References 8 publications

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“…The Emognition system [15] includes a mobile Android application with an embedded pre-trained ML model, a smartwatch application recording physiological signals, and a backend server storing all data. The smartwatch used in the framework is Samsung Galaxy Watch 3, and the smartphones are Android-based devices owned by the participants.…”

Section: Study Setup and Data A The Emognition Frameworkmentioning

confidence: 99%

“…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. Except for [2], [15], these studies did not try to recognize emotions in particular points in time but rather averaged over a longer period.…”

Section: Introduction and Related Workmentioning

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%

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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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Section: Study Setup and Data A The Emognition Frameworkmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

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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

Self Cite

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“…Until recently, however, the collection of rich multimodal emotion data was restricted to laboratory settings [2], [3], which limited the ability to recognize emotions in everyday life. Now, we can collect data related to emotions experienced in the field through a combination of the Experience Sampling Methods [4] and wearables [5]- [7]. The rapid development of wearable technologies and artificial intelligence (AI) opens new possibilities in affective science that overcome lab-based limitations [8]- [12].…”

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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“…It is widely used in various wearable devices, such as smartwatches and wristbands. Ability to measure ones PPG may be used to monitor their affective state, sleep quality, and other aspects impacting overall well-being [7][8][9][10][11]. Unfortunately, PPG is negatively affected by various types of disruptions related to device shifting around, changes in lighting, or the intensity and nature of body movements.…”

Section: Introductionmentioning

confidence: 99%

Processing Photoplethysmograms Recorded by Smartwatches to Improve the Quality of Derived Pulse Rate Variability

Polak¹,

Klich²,

Saganowski³

et al. 2022

Preprint

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Cardiac monitoring based on wearable photoplethysmography (PPG) is widespread because of its usability and low cost. Unfortunately, PPG is negatively affected by various types of disruptions, which could introduce errors to the algorithm that extracts Pulse Rate Variability (PRV). This study aims to identify the nature of such artifacts caused by various types of factors under the conditions of precisely planned experiments. We also propose methods for their reduction based solely on the PPG signal while preserving the frequency content of PRV. The accuracy of PRV derived from PPG was compared to Heart Rate Variability (HRV) derived from the accompanying recorded ECG. The results indicate that filtering PPG signals using the Discrete Wavelet Transform and its inverse (DWT/IDWT) is suitable for removing slow components and high-frequency noise. Moreover, the main benefit of amplitude demodulation is better preparation of the PPG to determine the duration of pulse cycles and reduce the impact of some other artifacts. Postprocessing applied to HRV and PRV indicates that the correction of outliers based on local statistical measures of signals and the AR model is only important when PPG is of low-quality and has no effect under good signal quality. The main conclusion is that DWT/IDWT, followed by amplitude demodulation, enables the proper preparation of the PPG signal for the subsequent use of PRV extraction algorithms. However, postprocessing in the proposed form should be applied more in the situations of observed strong artifacts than in motionless laboratory experiments.

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A system for collecting emotionally annotated physiological signals in daily life using wearables

Cited by 11 publications

References 8 publications

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

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

Ethical Considerations and Checklist for Affective Research With Wearables

Processing Photoplethysmograms Recorded by Smartwatches to Improve the Quality of Derived Pulse Rate Variability

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