# Real-time Automatic Emotion Recognition from Body Gestures

arXiv: https://arxiv.org/abs/1402.5047 · HTML: https://mlsanity.com/paper/1402.5047/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2014.

## Built on by (most cited first, 2 of 2)

- [MVRS: The Multimodal Virtual Reality Stimuli-based Emotion Recognition Dataset](https://mlsanity.com/paper/2509.05330/) (2025)
- [Learning Emotion from Motion: Kinetic Multi-Stream Skeleton Modeling with Metadata-Conditioned Weak Label Distributions](https://mlsanity.com/paper/2607.17121/) (2026)

## Abstract (arXiv)

Although psychological research indicates that bodily expressions convey important affective information, to date research in emotion recognition focused mainly on facial expression or voice analysis. In this paper we propose an approach to realtime automatic emotion recognition from body movements. A set of postural, kinematic, and geometrical features are extracted from sequences 3D skeletons and fed to a multi-class SVM classifier. The proposed method has been assessed on data acquired through two different systems: a professionalgrade optical motion capture system, and Microsoft Kinect. The system has been assessed on a "six emotions" recognition problem, and using a leave-one-subject-out cross validation strategy, reached an overall recognition rate of 61.3% which is very close to the recognition rate of 61.9% obtained by human observers. To provide further testing of the system, two games were developed, where one or two users have to interact to understand and express emotions with their body.