Consumer Wearables and Affective Computing for Wellbeing Support
Stanisław Saganowski, Przemysław Kazienko, Maciej Dzieżyc, Patrycja Jakimów, Joanna Komoszyńska, Weronika Michalska, Anna Dutkowiak, Adam Polak, Adam Dziadek, Michał Ujma
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Accepted to the International Workshop on Artificial Intelligence for Mobile and Ubiquitous Communication System, EAI MobiQuitous 2020
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.
A Formative Study of Brief Affective Text as a Complement to Wearable Sensing for Longitudinal Student Health Monitoring
University of Vermont（佛罗里达大学）；University of Vermont Department of Computer Science（佛罗里达大学计算机科学系）；University of Vermont Department of Psychological Science（佛罗里达大学心理学科学系）；University of Vermont Vermont Complex Systems Center（佛罗里达大学复杂系统中心）；University of Vermont Department of Rehabilitation（佛罗里达大学康复与运动科学系）；University of Vermont MassMutual Center of Excellence in Complex Systems（佛罗里达大学复杂系统与数据科学卓越中心）；Adelaide University School of Mathematical Sciences（阿德莱德大学数学科学学院）；University of Vermont Department of Mathematics（佛罗里达大学数学系）；Adelaide University（阿德莱德大学）
Emotion Recognition with Minimal Wearable Sensing: Multi-domain Feature, Hybrid Feature Selection, and Personalized vs. Generalized Ensemble Model Analysis
Recording Brain Activity While Listening to Music Using Wearable EEG Devices Combined with Bidirectional Long Short-Term Memory Networks
School of Music, Jiangxi Normal University（江西师范大学音乐学院）；School of Electronic Information, HuZhou College（湖州学院电子信息学院）；San Francisco State University（旧金山州立大学）
Objective Prediction of Tomorrow's Affect Using Multi-Modal Physiological Data and Personal Chronicles: A Study of Monitoring College Student Well-being in 2020
University of California Irvine（加州大学尔湾分校）；University of California, Irvine（加州大学尔湾分校）
