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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Datasets for Automated Affect and Emotion Recognition from Cardiovascular Signals Using Artificial Intelligence— A Systematic Review
Paweł Jemioło,Dawid Storman,Maria Mamica,Mateusz Szymkowski,Wioletta Żabicka,Magdalena Wojtaszek-Główka,Antoni Ligęza +6 more
TL;DR: A review of publicly available datasets used for automated affect and emotion recognition (AAER) with artificial intelligence (AI) techniques, and emphasising cardiovascular (CV) signals found that the quality of the analysed papers was mainly low.
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SATO (IDEAS expAnded wiTh BCIO): Workflow for designers of patient-centered mobile health behaviour change intervention applications
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Sleep detection using physiological signals from a wearable device
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Semi-Supervised Generative Adversarial Network for Stress Detection Using Partially Labeled Physiological Data
TL;DR: This paper shows that Semi-Supervised algorithms are a viable method for inexpensive affective state detection systems with accurate results and a comparison of a fully supervised and semi-supervised algorithms for stress classification, and shows its effectiveness.
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Context-Based Emotion Predictor: A Decision- Making Framework for Mobile Data
Zahid Anwar,Rashidihor Jahangir,Muhammad Asif Nauman,Roobaea Alroobaea,Sabah Alzahrani,Ihsan Ali +5 more
TL;DR: This study proposes a decision-making framework for efficient emotion detection of mobile reviews and outperformed the existing emotion detection systems by achieving 86% accuracy over mobile reviews.
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