Emognition dataset: emotion recognition with self-reports, facial expressions, and physiology using wearables

Shu, L. et al. Wearable emotion recognition using heart rate data from a smart bracelet. Sensors 20, 718 (2020).

Feng, H., Golshan, H. M. & Mahoor, M. H. A wavelet-based approach to emotion classification using eda signals. Expert Systems with Applications 112, 77–86 (2018).

Bleidorn, W. et al. The healthy personality from a basic trait perspective. Journal of personality and social psychology 118, 1207 (2020).

Smith, A. M. et al. Coping with health threats: the costs and benefits of managing emotions. Psychological science 32, 1011–1023 (2021).

Tizzano, G. R., Spezialetti, M. & Rossi, S. A deep learning approach for mood recognition from wearable data. In 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 1–5 (IEEE, 2020).

Nalepa, G. J., Kutt, K., Giżycka, B., Jemiolo, P. & Bobek, S. Analysis and use of the emotional context with wearable devices for games and intelligent assistants. Sensors 19, 2509 (2019).

Lisetti, C. L. & Nasoz, F. Categorizing autonomic nervous system (ans) emotional signals using bio-sensors for hri within the maui paradigm. In ROMAN 2006-The 15th IEEE Int. Symp. on Robot and Human Interactive Communication, 277–284 (IEEE, 2006).

Kutt, K., Nalepa, G. J., Giżycka, B., Jemiolo, P. & Adamczyk, M. Bandreader-a mobile application for data acquisition from wearable devices in affective computing experiments. In 2018 11th International Conference on Human System Interaction (HSI), 42–48 (IEEE, 2018).

Behnke, M., Buchwald, M., Bykowski, A., Kupinski, S. & Kaczmarek, L. Psychophysiology of positive and negative emotions, dataset of 1157 cases and 8 biosignals. Scientific Data (2022).

Kutt, K. et al. Biraffe: Bio-reactions and faces for emotion-based personalization. In Proceedings of the 3rd Workshop on Affective Computing and Context Awareness in Ambient Intelligence (AfCAI 2019) (Aachen: Technical University of Aachen, 2019).

Gupta, R. et al. A quality adaptive multimodal affect recognition system for user-centric multimedia indexing. In Proceedings of the 2016 ACM on international conference on multimedia retrieval, 317–320 (2016).

Subramanian, R. et al. Ascertain: Emotion and personality recognition using commercial sensors. IEEE Transactions on Affective Computing 9, 147–160 (2016).

Abadi, M. K. et al. Decaf: Meg-based multimodal database for decoding affective physiological responses. IEEE Transactions on Affective Computing 6, 209–222 (2015).

Soleymani, M., Lichtenauer, J., Pun, T. & Pantic, M. A multimodal database for affect recognition and implicit tagging. IEEE transactions on affective computing 3, 42–55 (2011).

Koelstra, S. et al. Deap: A database for emotion analysis; using physiological signals. IEEE transactions on affective computing 3, 18–31 (2011).

Kreibig, S. D. Autonomic nervous system activity in emotion: A review. Biological psychology 84, 394–421 (2010).

Kreibig, S. D., Gendolla, G. H. & Scherer, K. R. Psychophysiological effects of emotional responding to goal attainment. Biological Psychology 84, 474–487 (2010).

Behnke, M., Kreibig, S. D., Kaczmarek, L. D., Assink, M. & Gross, J. J. Positive emotions and autonomic nervous system reactivity: A meta-analytical review. Emotion Review (2022).

Hewig, J. et al. A revised film set for the induction of basic emotions. Cognition and emotion 19, 1095 (2005).

Kaczmarek, L. D. et al. Splitting the affective atom: Divergence of valence and approach-avoidance motivation during a dynamic emotional experience. Current Psychology 1–12 (2019).

Gross, J. J. & Levenson, R. W. Emotion elicitation using films. Cognition & emotion 9, 87–108 (1995).

Schaefer, A., Nils, F., Sanchez, X. & Philippot, P. Assessing the effectiveness of a large database of emotion-eliciting films: A new tool for emotion researchers. Cognition and emotion 24, 1153–1172 (2010).

Reynaud, E., El-Khoury-Malhame, M., Blin, O. & Khalfa, S. Voluntary emotion suppression modifies psychophysiological responses to films. Journal of Psychophysiology 26, 116 (2012).

Kaczmarek, L. D. et al. High-approach and low-approach positive affect influence physiological responses to threat and anger. International Journal of Psychophysiology 138, 27–37 (2019).

Stephens, C. L., Christie, I. C. & Friedman, B. H. Autonomic specificity of basic emotions: Evidence from pattern classification and cluster analysis. Biological psychology 84, 463–473 (2010).

Christie, I. C. & Friedman, B. H. Autonomic specificity of discrete emotion and dimensions of affective space: A multivariate approach. International journal of psychophysiology 51, 143–153 (2004).

Nyklíček, I., Thayer, J. F. & Van Doornen, L. J. Cardiorespiratory differentiation of musically-induced emotions. Journal of Psychophysiology (1997).

Bradley, M. M. & Lang, P. J. Measuring emotion: the self-assessment manikin and the semantic differential. Journal of behavior therapy and experimental psychiatry 25, 49–59 (1994).

Behnke, M., Gross, J. J. & Kaczmarek, L. D. The role of emotions in esports performance. Emotion (2020).

Marchewka, A., Żurawski, Ł., Jednoróg, K. & Grabowska, A. The nencki affective picture system (naps): Introduction to a novel, standardized, wide-range, high-quality, realistic picture database. Behavior research methods 46, 596–610 (2014).

Empatica Development Team. Empatica E4 SDK for Android. http://developer.empatica.com/android-sdk-tutorial-100.html. (Online; accessed March 2021).

Ratti, E., Waninger, S., Berka, C., Ruffini, G. & Verma, A. Comparison of medical and consumer wireless eeg systems for use in clinical trials. Frontiers in human neuroscience 11, 398 (2017).

Krigolson, O. E., Williams, C. C., Norton, A., Hassall, C. D. & Colino, F. L. Choosing muse: Validation of a low-cost, portable eeg system for erp research. Frontiers in neuroscience 11, 109 (2017).

Raheel, A., Majid, M., Alnowami, M. & Anwar, S. M. Physiological sensors based emotion recognition while experiencing tactile enhanced multimedia. Sensors 20, 4037 (2020).

Schuurmans, A. A. et al. Validity of the empatica e4 wristband to measure heart rate variability (hrv) parameters: A comparison to electrocardiography (ecg). Journal of medical systems 44, 1–11 (2020).

Ragot, M., Martin, N., Em, S., Pallamin, N. & Diverrez, J.-M. Emotion recognition using physiological signals: laboratory vs. wearable sensors. In International Conference on Applied Human Factors and Ergonomics, 15–22 (Springer, 2017).

Dzieżyc, M. et al. How to catch them all? enhanced data collection for emotion recognition in the field. In 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 348–351 (IEEE, 2021).

Saganowski, S. et al. Consumer wearables and affective computing for wellbeing support. In MobiQuitous 2020 - 17th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 482–487 (ACM, 2020).

Avram, R. et al. Validation of an algorithm for continuous monitoring of atrial fibrillation using a consumer smartwatch. Heart Rhythm (2021).

Mehrabadi, M. A. et al. Sleep tracking of a commercially available smart ring and smartwatch against medical-grade actigraphy in everyday settings: instrument validation study. JMIR mHealth and uHealth 8, e20465 (2020).

Saganowski, S. et al. A system for collecting emotionally annotated physiological signals in daily life using wearables. In 9th International Conference on Affective Computing and Intelligent Interaction (ACII 2021) (IEEE, 2021).

Baltrusaitis, T., Zadeh, A., Lim, Y. C. & Morency, L.-P. Openface 2.0: Facial behavior analysis toolkit. In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), 59–66 (IEEE, 2018).

Zadeh, A., Chong Lim, Y., Baltrusaitis, T. & Morency, L.-P. Convolutional experts constrained local model for 3d facial landmark detection. In Proceedings of the IEEE International Conference on Computer Vision Workshops, 2519–2528 (2017).

Baltrušaitis, T., Mahmoud, M. & Robinson, P. Cross-dataset learning and person-specific normalisation for automatic action unit detection. In 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 6, 1–6 (IEEE, 2015).

Saganowski, S. et al. Emognition Wearable Dataset 2020. Harvard Dataverse https://doi.org/10.7910/DVN/R9WAF4 (2021).

Richardson, J. T. Eta squared and partial eta squared as measures of effect size in educational research. Educational research review 6, 135–147 (2011).

Lakens, D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and anovas. Frontiers in psychology 4, 863 (2013).

Cohen, J. Statistical power analysis for the social sciences (Hillsdale, NJ: Erlbaum, 1988).

Saganowski, S. et al. Emotion recognition using wearables: A systematic literature review-work-in-progress. In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 1–6 (IEEE, 2020).

Bizzego, A., Battisti, A., Gabrieli, G., Esposito, G. & Furlanello, C. pyphysio: A physiological signal processing library for data science approaches in physiology. SoftwareX 10, 100287 (2019).

Benedek, M. & Kaernbach, C. A continuous measure of phasic electrodermal activity. Journal of neuroscience methods 190, 80–91 (2010).

Ko, B. C. A brief review of facial emotion recognition based on visual information. sensors 18, 401 (2018).

Ekman, P. & Friesen, W. V. Facial action coding system: Investigator’s guide (Consulting Psychologists Press, 1978).