The AffectToolbox: Affect Analysis for Everyone

In the field of affective computing, where research continually advances at a rapid pace, the demand for user-friendly tools has become increasingly apparent. In this paper, we present the AffectToolbox, a novel software system that aims to support researchers in developing affect-sensitive studies and prototypes. The proposed system addresses the challenges posed by existing frameworks, which often require profound programming knowledge and cater primarily to power-users or skilled developers. Aiming to facilitate ease of use, the AffectToolbox requires no programming knowledge and offers its functionality to reliably analyze the affective state of users through an accessible graphical user interface. The architecture encompasses a variety of models for emotion recognition on multiple affective channels and modalities, as well as an elaborate fusion system to merge multi-modal assessments into a unified result. The entire system is open-sourced and will be publicly available to ensure easy integration into more complex applications through a well-structured, Python-based code base - therefore marking a substantial contribution toward advancing affective computing research and fostering a more collaborative and inclusive environment within this interdisciplinary field.

Authors

Institutions

Publication Details

Published
2024-09-15
DOI
https://doi.org/10.1109/acii63134.2024.00026
Citations
1
Primary Topic
Cognitive and psychological constructs research
Type
article
Field-Weighted Citation Impact
0.64

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The AffectToolbox: Affect Analysis for Everyone

Michael Dietz, Florian Lingenfelser, Silvan Mertes, Elisabeth André et al.
1 citations
Cognitive and psychological constructs research
0.64
article

The AffectToolbox: Affect Analysis for Everyone

Michael Dietz, Florian Lingenfelser, Silvan Mertes, Elisabeth André, Dominik Schiller
article en
1 citations

Abstract

In the field of affective computing, where research continually advances at a rapid pace, the demand for user-friendly tools has become increasingly apparent. In this paper, we present the AffectToolbox, a novel software system that aims to support researchers in developing affect-sensitive studies and prototypes. The proposed system addresses the challenges posed by existing frameworks, which often require profound programming knowledge and cater primarily to power-users or skilled developers. Aiming to facilitate ease of use, the AffectToolbox requires no programming knowledge and offers its functionality to reliably analyze the affective state of users through an accessible graphical user interface. The architecture encompasses a variety of models for emotion recognition on multiple affective channels and modalities, as well as an elaborate fusion system to merge multi-modal assessments into a unified result. The entire system is open-sourced and will be publicly available to ensure easy integration into more complex applications through a well-structured, Python-based code base - therefore marking a substantial contribution toward advancing affective computing research and fostering a more collaborative and inclusive environment within this interdisciplinary field.

University of Augsburg (DE)
Bundesministerium für Bildung und Forschung
Openalex Percentile: Top 23%
Cognitive and psychological constructs research
0.64
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

The AffectToolbox: Affect Analysis for Everyone — Michael Dietz, Florian Lingenfelser, et al. · (2024) | TGRS Research Map | TGRS