# A dataset of continuous affect annotations and physiological signals for emotion analysis

arXiv: https://arxiv.org/abs/1812.02782 · HTML: https://mlsanity.com/paper/1812.02782/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2018.

## Built on by (most cited first, 19 of 19)

- [Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition](https://mlsanity.com/paper/2303.17611/) (2023)
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- [A Survey of Few-Shot Learning for Biomedical Time Series](https://mlsanity.com/paper/2405.02485/) (2024)
- [AttX: Attentive Cross-Connections for Fusion of Wearable Signals in Emotion Recognition](https://mlsanity.com/paper/2206.04625/) (2022)
- [GameVibe: A Multimodal Affective Game Corpus](https://mlsanity.com/paper/2407.12787/) (2024)
- [Multi-level Stress Assessment from ECG in a Virtual Reality Environment using Multimodal Fusion](https://mlsanity.com/paper/2107.04566/) (2021)
- [Multi-scale Transformer-based Network for Emotion Recognition from Multi Physiological Signals](https://mlsanity.com/paper/2305.00769/) (2023)
- [Investigating the Generalizability of Physiological Characteristics of Anxiety](https://mlsanity.com/paper/2402.15513/) (2024)
- [Data Fusion in Neuromarketing: Multimodal Analysis of Biosignals, Lifecycle Stages, Current Advances, Datasets, Trends, and Challenges](https://mlsanity.com/paper/2209.00993/) (2022)
- [Deep-seeded Clustering for Emotion Recognition from Wearable Physiological Sensors](https://mlsanity.com/paper/2308.09013/) (2023)
- [Ensemble Learning to Assess Dynamics of Affective Experience Ratings and Physiological Change](https://mlsanity.com/paper/2312.16036/) (2023)
- [AffectEval: A Modular and Customizable Framework for Affective Computing](https://mlsanity.com/paper/2504.21184/) (2025)
- [Challenges and Limitations of Generative AI in Synthesizing Wearable Sensor Data](https://mlsanity.com/paper/2505.14206/) (2025)
- [AnnoSense: A Framework for Physiological Emotion Data Collection in Everyday Settings for AI](https://mlsanity.com/paper/2508.02680/) (2025)
- [Reproducible Physiological Features in Affective Computing: A Preliminary Analysis on Arousal Modeling](https://mlsanity.com/paper/2508.10561/) (2025)
- [Long-Term Variability in Physiological-Arousal Relationships for Robust Emotion Estimation](https://mlsanity.com/paper/2508.18782/) (2025)
- [PREFAB: PREFerence-based Affective Modeling for Low-Budget Self-Annotation](https://mlsanity.com/paper/2601.13904/) (2026)
- [Modeling Shared and Individual Structure for Cross-Subject Continuous Affect Regression from EEG-fNIRS](https://mlsanity.com/paper/2610.02796/) (2026)

## Abstract (arXiv)

From a computational viewpoint, emotions continue to be intriguingly hard to understand. In research, direct, real-time inspection in realistic settings is not possible. Discrete, indirect, post-hoc recordings are therefore the norm. As a result, proper emotion assessment remains a problematic issue. The Continuously Annotated Signals of Emotion (CASE) dataset provides a solution as it focusses on real-time continuous annotation of emotions, as experienced by the participants, while watching various videos. For this purpose, a novel, intuitive joystick-based annotation interface was developed, that allowed for simultaneous reporting of valence and arousal, that are instead often annotated independently. In parallel, eight high quality, synchronized physiological recordings (1000 Hz, 16-bit ADC) were made of ECG, BVP, EMG (3x), GSR (or EDA), respiration and skin temperature. The dataset consists of the physiological and annotation data from 30 participants, 15 male and 15 female, who watched several validated video-stimuli. The validity of the emotion induction, as exemplified by the annotation and physiological data, is also presented.