Consumer Wearables and Affective Computing for Wellbeing Support
Stanisław Saganowski,Przemysław Kazienko,Maciej Dziezyc,Patrycja Jakimów,Joanna Komoszynska,Weronika Michalska,Anna Dutkowiak,Adam G. Polak,Adam Dziadek,Michal Ujma +9 more
TLDR
There is no versatile device suitable for all purposes in the field of wellbeing support, and the WellAff system able to recognize affective states for wellbeing support is proposed.Abstract:
Wearables equipped with pervasive sensors enable us to monitor physiological and behavioral signals in our everyday life. We propose the WellAff system able to recognize affective states for wellbeing support. It also includes health care scenarios, in particular patients with chronic kidney disease (CKD) suffering from bipolar disorders. For the need of a large-scale field study, we revised over 50 off-the-shelf devices in terms of usefulness for emotion, stress, meditation, sleep, and physical activity recognition and analysis. Their usability directly comes from the types of sensors they possess as well as the quality and availability of raw signals. We found there is no versatile device suitable for all purposes. Using Empatica E4 and Samsung Galaxy Watch, we have recorded physiological signals from 11 participants over many weeks. The gathered data enabled us to train a classifier that accurately recognizes strong affective states.read more
Citations
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Journal ArticleDOI
Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data.
TL;DR: The experimental results showed that the CNN-based architectures might be more suitable than LSTM-based architecture for affect recognition from physiological sensors, and the performance of the models depends on the intensity of the physiological response induced by the affective stimuli.
Journal ArticleDOI
Emognition dataset: emotion recognition with self-reports, facial expressions, and physiology using wearables
Stanisław Saganowski,Joanna Komoszynska,Maciej Behnke,Bartosz Perz,Dominika Kunc,Bartłomiej Klich,Łukasz D. Kaczmarek,Przemysław Kazienko +7 more
TL;DR: In this paper , the Emognition dataset is dedicated to testing methods for emotion recognition (ER) from physiological responses and facial expressions from short film clips eliciting nine discrete emotions: amusement, awe, enthusiasm, liking, surprise, anger, disgust, fear, and sadness.
Journal ArticleDOI
Emognition dataset: emotion recognition with self-reports, facial expressions, and physiology using wearables
Stanisław Saganowski,Joanna Komoszynska,Maciej Behnke,Bartosz Perz,Dominika Kunc,Bartłomiej Klich,Łukasz D. Kaczmarek,Przemysław Kazienko +7 more
TL;DR: In this paper , the Emognition dataset is dedicated to testing methods for emotion recognition (ER) from physiological responses and facial expressions from short film clips eliciting nine discrete emotions: amusement, awe, enthusiasm, liking, surprise, anger, disgust, fear, and sadness.
Journal ArticleDOI
The Feasibility of Wearable and Self-Report Stress Detection Measures in a Semi-Controlled Lab Environment
Sara Aristizabal,Kunjoon Byun,Nadia Wood,Aidan F. Mullan,Paige Porter,Carolina Campanella,Anja Jamrozik,Ivan Z. Nenadic,Brent A. Bauer +8 more
TL;DR: In this article, the authors evaluated the feasibility of detecting stress using deep learning, a subfield of machine learning, on a small data set consisting of electrodermal activity, skin temperature, and heart rate measurements, in combination with selfreported anxiety and stress.
Journal ArticleDOI
The Cold Start Problem and Per-Group Personalization in Real-Life Emotion Recognition With Wearables
Stanisław Saganowski,Dominika Kunc,Bartosz Perz,Joanna Komoszynska,Maciej Behnke,Przemysław Kazienko +5 more
TL;DR: 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, and investigates the potential of per-group personalization and the amount of data needed to perform it.
References
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