# Consumer Wearables and Affective Computing for Wellbeing Support

arXiv: https://arxiv.org/abs/2005.00093 · HTML: https://mlsanity.com/paper/2005.00093/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Semi-Supervised Generative Adversarial Network for Stress Detection Using Partially Labeled Physiological Data](https://mlsanity.com/paper/2206.14976/) (2022)
- [Can gamification reduce the burden of self-reporting in mHealth applications? A feasibility study using machine learning from smartwatch data to estimate cognitive load](https://mlsanity.com/paper/2302.03616/) (2023)
- [Decoding Emotional Valence from Wearables: Can Our Data Reveal Our True Feelings?](https://mlsanity.com/paper/2401.05408/) (2023)

## Abstract (arXiv)

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.