research-article

Emotion Expression in Human Body Posture and Movement: A Survey on Intelligible Motion Factors, Quantification and Validation

Authors:

Mehdi-Antoine Mahfoudi,

Alexandre Meyer,

Thibaut Gaudin,

Saida BouakazAuthors Info & Claims

IEEE Transactions on Affective Computing, Volume 14, Issue 4

Pages 2697 - 2721

https://doi.org/10.1109/TAFFC.2022.3226252

Published: 01 October 2023 Publication History

Abstract

Many areas in computer science are facing the need to analyze, quantify and reproduce movements expressing emotions. This paper presents a systematic review of the intelligible factors involved in the expression of emotions in human movement and posture. We have gathered the works that have studied and tried to identify these factors by sweeping many disciplinary fields such as psychology, biomechanics, choreography, robotics and computer vision. These researches have each used their own definitions, units and emotions, which prevents a global and coherent vision. We propose a meta-analysis approach that cross-references and aggregates these researches in order to have a unified list of expressive factors quantified for each emotion. A calculation method is then proposed for each of the expressive factors and we extract them from an emotionally annotated animation dataset: Emilya. The comparison between the results of the meta-analysis and the Emilya analysis reveals high correlation rates, which validates the relevance of the quantified values obtained by both methodologies. The analysis of the results raises interesting perspectives for future research in affective computing.

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Cited By

Canovi NMontagna FNiewiadomski RSciutti ADi Cesare GBeyan C(2024)Diffusion-Based Unsupervised Pre-training for Automated Recognition of Vitality FormsProceedings of the 2024 International Conference on Advanced Visual Interfaces10.1145/3656650.3656689(1-9)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3656650.3656689
Yang DYang KKuang HChen ZWang YZhang L(2024)Towards Context-Aware Emotion Recognition Debiasing From a Causal Demystification Perspective via De-Confounded TrainingIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.344312946:12(10663-10680)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TPAMI.2024.3443129
Wang TLiu SHe FDai WDu MKe YMing D(2024)Emotion Recognition From Full-Body Motion Using Multiscale Spatio-Temporal NetworkIEEE Transactions on Affective Computing10.1109/TAFFC.2023.330519715:3(898-912)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TAFFC.2023.3305197

Index Terms

Emotion Expression in Human Body Posture and Movement: A Survey on Intelligible Motion Factors, Quantification and Validation

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Affective Computing

IEEE Transactions on Affective Computing Volume 14, Issue 4

Oct.-Dec. 2023

832 pages

ISSN:1949-3045

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1949-3045 © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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IEEE Computer Society Press

Washington, DC, United States

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Published: 01 October 2023

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Cited By

Canovi NMontagna FNiewiadomski RSciutti ADi Cesare GBeyan C(2024)Diffusion-Based Unsupervised Pre-training for Automated Recognition of Vitality FormsProceedings of the 2024 International Conference on Advanced Visual Interfaces10.1145/3656650.3656689(1-9)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3656650.3656689
Yang DYang KKuang HChen ZWang YZhang L(2024)Towards Context-Aware Emotion Recognition Debiasing From a Causal Demystification Perspective via De-Confounded TrainingIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.344312946:12(10663-10680)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TPAMI.2024.3443129
Wang TLiu SHe FDai WDu MKe YMing D(2024)Emotion Recognition From Full-Body Motion Using Multiscale Spatio-Temporal NetworkIEEE Transactions on Affective Computing10.1109/TAFFC.2023.330519715:3(898-912)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TAFFC.2023.3305197

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