# Emotion Recognition From Gait Analyses: Current Research and Future Directions

arXiv: https://arxiv.org/abs/2003.11461 · HTML: https://mlsanity.com/paper/2003.11461/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2020.

## Built on by (most cited first, 11 of 11)

- [Benchmarking Micro-action Recognition: Dataset, Methods, and Applications](https://mlsanity.com/paper/2403.05234/) (2024)
- [MMAD: Multi-label Micro-Action Detection in Videos](https://mlsanity.com/paper/2407.05311/) (2024)
- [GaitFormer: Learning Gait Representations with Noisy Multi-Task Learning](https://mlsanity.com/paper/2310.19418/) (2023)
- [TNTC: two-stream network with transformer-based complementarity for gait-based emotion recognition](https://mlsanity.com/paper/2110.13708/) (2021)
- [From Face to Gait: Weakly-Supervised Learning of Gender Information from Walking Patterns](https://mlsanity.com/paper/2111.00538/) (2021)
- [Hybrid-supervised Hypergraph-enhanced Transformer for Micro-gesture Based Emotion Recognition](https://mlsanity.com/paper/2507.14867/) (2025)
- [SeLiNet: Sentiment enriched Lightweight Network for Emotion Recognition in Images](https://mlsanity.com/paper/2307.02773/) (2023)
- [Emotion Recognition from Skeleton Data: A Comprehensive Survey](https://mlsanity.com/paper/2507.18026/) (2025)
- [Towards Explainable, Privacy-Preserved Human-Motion Affect Recognition](https://mlsanity.com/paper/2105.03958/) (2021)
- [Trust model of privacy-concerned, emotionally-aware agents in a cooperative logistics problem](https://mlsanity.com/paper/2401.14436/) (2024)
- [CGTGait: Collaborative Graph and Transformer for Gait Emotion Recognition](https://mlsanity.com/paper/2509.16623/) (2025)

## Abstract (arXiv)

Human gait refers to a daily motion that represents not only mobility, but it can also be used to identify the walker by either human observers or computers. Recent studies reveal that gait even conveys information about the walker's emotion. Individuals in different emotion states may show different gait patterns. The mapping between various emotions and gait patterns provides a new source for automated emotion recognition. Compared to traditional emotion detection biometrics, such as facial expression, speech and physiological parameters, gait is remotely observable, more difficult to imitate, and requires less cooperation from the subject. These advantages make gait a promising source for emotion detection. This article reviews current research on gait-based emotion detection, particularly on how gait parameters can be affected by different emotion states and how the emotion states can be recognized through distinct gait patterns. We focus on the detailed methods and techniques applied in the whole process of emotion recognition: data collection, preprocessing, and classification. At last, we discuss possible future developments of efficient and effective gait-based emotion recognition using the state of the art techniques on intelligent computation and big data.