Real-Life Validation of Emotion Detection System with Wearables

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

doi:10.1007/978-3-031-06527-9_5

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…For instance, one of the judges did not agree that study-related technology might elicit frustration or anger, so it does not need to be classified as an ethical risk. We disagreed with this comment, as we observed in our studies that malfunctioning technology causes frustration, anger, and some discomfort in participants [53]. We also disagreed with a comment con-cerning rewards.…”

Section: Resultsmentioning

confidence: 65%

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: Resultsmentioning

confidence: 65%

Ethical Considerations and Checklist for Affective Research With Wearables

Behnke

Saganowski

Kunc

et al. 2024

IEEE Trans. Affective Comput.

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“…These improvements facilitate emotion recognition from both facial expression analysis and physiological perspectives, accommodating variances that might occur with specific emotions. While several studies have utilized the physiological signals from the Emognition dataset to classify emotions (e.g., [25][26][27][28] ), there has been relatively little research examining the facial expression data within the same dataset for emotion recognition and classification (e.g., 29 . This gap highlights a key area for further investigation, aiming to fully leverage the dataset's capabilities in enhancing FER technologies.…”

Section: Related Workmentioning

confidence: 99%

Image-based facial emotion recognition using convolutional neural network on emognition dataset

Agung,

Rifai,

Wijayanto

2024

Sci Rep

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Detecting emotions from facial images is difficult because facial expressions can vary significantly. Previous research on using deep learning models to classify emotions from facial images has been carried out on various datasets that contain a limited range of expressions. This study expands the use of deep learning for facial emotion recognition (FER) based on Emognition dataset that includes ten target emotions: amusement, awe, enthusiasm, liking, surprise, anger, disgust, fear, sadness, and neutral. A series of data preprocessing was carried out to convert video data into images and augment the data. This study proposes Convolutional Neural Network (CNN) models built through two approaches, which are transfer learning (fine-tuned) with pre-trained models of Inception-V3 and MobileNet-V2 and building from scratch using the Taguchi method to find robust combination of hyperparameters setting. The proposed model demonstrated favorable performance over a series of experimental processes with an accuracy and an average F1-score of 96% and 0.95, respectively, on the test data.

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Unsupervised learning for physiological signals in real-life emotion recognition using wearables

Kune

2022

2022 10th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)

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Real-Life Validation of Emotion Detection System with Wearables

Cited by 5 publications

References 15 publications

Ethical Considerations and Checklist for Affective Research With Wearables

Ethical Considerations and Checklist for Affective Research With Wearables

Image-based facial emotion recognition using convolutional neural network on emognition dataset

Unsupervised learning for physiological signals in real-life emotion recognition using wearables

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