# AffectEval: A Modular and Customizable Framework for Affective Computing

arXiv: https://arxiv.org/abs/2504.21184 · HTML: https://mlsanity.com/paper/2504.21184/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 49 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [A dataset of continuous affect annotations and physiological signals for emotion analysis](https://mlsanity.com/paper/1812.02782/) (2018)
- [Affective Computing for Healthcare: Recent Trends, Applications, Challenges, and Beyond](https://mlsanity.com/paper/2402.13589/) (2024)
- [The AffectToolbox: Affect Analysis for Everyone](https://mlsanity.com/paper/2402.15195/) (2024)
- [Investigating the Generalizability of Physiological Characteristics of Anxiety](https://mlsanity.com/paper/2402.15513/) (2024)

## Abstract (arXiv)

The field of affective computing focuses on recognizing, interpreting, and responding to human emotions, and has broad applications across education, child development, and human health and wellness. However, developing affective computing pipelines remains labor-intensive due to the lack of software frameworks that support multimodal, multi-domain emotion recognition applications. This often results in redundant effort when building pipelines for different applications. While recent frameworks attempt to address these challenges, they remain limited in reducing manual effort and ensuring cross-domain generalizability. We introduce AffectEval, a modular and customizable framework to facilitate the development of affective computing pipelines while reducing the manual effort and duplicate work involved in developing such pipelines. We validate AffectEval by replicating prior affective computing experiments, and we demonstrate that our framework reduces programming effort by up to 90%, as measured by the reduction in raw lines of code.