Consumer Wearables and Affective Computing for Wellbeing Support
This work addresses wellbeing monitoring for individuals, particularly patients with chronic conditions like CKD and bipolar disorders, but is incremental as it builds on existing wearable technology and affective computing methods.
The authors tackled the problem of recognizing affective states for wellbeing support using consumer wearables, finding no single device suitable for all purposes and training a classifier that accurately recognizes strong affective states from data collected from 11 participants.
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.