# Wearable affect and stress recognition: A review

arXiv: https://arxiv.org/abs/1811.08854 · HTML: https://mlsanity.com/paper/1811.08854/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2018.

## Built on by (most cited first, 5 of 5)

- [Analysing the Performance of Stress Detection Models on Consumer-Grade Wearable Devices](https://mlsanity.com/paper/2203.09669/) (2022)
- [An Improved Subject-Independent Stress Detection Model Applied to Consumer-grade Wearable Devices](https://mlsanity.com/paper/2203.09663/) (2022)
- [Personalization of Stress Mobile Sensing using Self-Supervised Learning](https://mlsanity.com/paper/2308.02731/) (2023)
- [Multimodal signal fusion for stress detection using deep neural networks: a novel approach for converting 1D signals to unified 2D images](https://mlsanity.com/paper/2509.13636/) (2025)
- [RETRACE: Resilience-Guided Trait-Conditioned Craving Estimation from Wearable Physiology in Opioid Use Disorder](https://mlsanity.com/paper/2608.14947/) (2026)

## Abstract (arXiv)

Affect recognition aims to detect a person's affective state based on observables, with the goal to e.g. provide reasoning for decision making or support mental wellbeing. Recently, besides approaches based on audio, visual or text information, solutions relying on wearable sensors as observables (recording mainly physiological and inertial parameters) have received increasing attention. Wearable systems offer an ideal platform for long-term affect recognition applications due to their rich functionality and form factor. However, existing literature lacks a comprehensive overview of state-of-the-art research in wearable-based affect recognition. Therefore, the aim of this paper is to provide a broad overview and in-depth understanding of the theoretical background, methods, and best practices of wearable affect and stress recognition. We summarise psychological models, and detail affect-related physiological changes and their measurement with wearables. We outline lab protocols eliciting affective states, and provide guidelines for ground truth generation in field studies. We also describe the standard data processing chain, and review common approaches to preprocessing, feature extraction, and classification. By providing a comprehensive summary of the state-of-the-art and guidelines to various aspects, we would like to enable other researchers in the field of affect recognition to conduct and evaluate user studies and develop wearable systems.