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

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

doi:10.1109/percomworkshops53856.2022.9767233

Cited by 10 publications

(3 citation statements)

References 31 publications

(31 reference statements)

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“…If the study use AI models trained on the general population dataset (e.g., due to the lack of personalized data -cold start problem [51]), then some individual differences (e.g., in emotional responses and evaluations) might reduce the models' usability and leads to models' incorrect predictions. Even for a given individual, their variability of physiology and perception may depend on the time and context.…”

Section: Risk 29: Overgeneralization Of Individualsmentioning

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: Risk 29: Overgeneralization Of Individualsmentioning

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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“…It is widely used in various wearable devices, such as smartwatches and wristbands. The ability to measure someone's PPG may be used to monitor her/his 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

Polak

Klich

Saganowski

et al. 2022

Sensors

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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 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. Post-processing applied to HRV and PRV indicates that the correction of outliers based on local statistical measures of signals and the autoregressive (AR) model is only important when the PPG is of low quality and has no effect under good signal quality. The main conclusion is that the DWT/IDWT, followed by amplitude demodulation, enables the proper preparation of the PPG signal for the subsequent use of PRV extraction algorithms, particularly at rest. However, post-processing in the proposed form should be applied more in the situations of observed strong artifacts than in motionless laboratory experiments.

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

Cited by 10 publications

References 31 publications

Ethical Considerations and Checklist for Affective Research 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

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

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