# PsyMo: A Dataset for Estimating Self-Reported Psychological Traits from Gait

arXiv: https://arxiv.org/abs/2308.10631 · HTML: https://mlsanity.com/paper/2308.10631/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 87 references that are in the map.
- First posted on arXiv 21 Aug 2023.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Hybrid Task Cascade for Instance Segmentation](https://mlsanity.com/paper/1901.07518/) (2019)
- [CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation](https://mlsanity.com/paper/2208.00571/) (2022)
- [CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark](https://mlsanity.com/paper/1812.00324/) (2018)
- [GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition](https://mlsanity.com/paper/1811.06186/) (2018)
- [Gait Recognition via Effective Global-Local Feature Representation and Local Temporal Aggregation](https://mlsanity.com/paper/2011.01461/) (2020)
- [GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition](https://mlsanity.com/paper/2101.11228/) (2021)
- [Gait Recognition in the Wild with Dense 3D Representations and A Benchmark](https://mlsanity.com/paper/2204.02569/) (2022)
- [On Learning Disentangled Representations for Gait Recognition](https://mlsanity.com/paper/1909.03051/) (2019)
- [JointsGait:A model-based Gait Recognition Method based on Gait Graph Convolutional Networks and Joints Relationship Pyramid Mapping](https://mlsanity.com/paper/2005.08625/) (2020)
- [First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis](https://mlsanity.com/paper/1804.08046/) (2018)
- [Gait Recognition Using 3-D Human Body Shape Inference](https://mlsanity.com/paper/2212.09042/) (2022)
- [Take an Emotion Walk: Perceiving Emotions from Gaits Using Hierarchical Attention Pooling and Affective Mapping](https://mlsanity.com/paper/1911.08708/) (2019)
- [WildGait: Learning Gait Representations from Raw Surveillance Streams](https://mlsanity.com/paper/2105.05528/) (2021)
- [From Face to Gait: Weakly-Supervised Learning of Gender Information from Walking Patterns](https://mlsanity.com/paper/2111.00538/) (2021)
- [Data Augmentation for Depression Detection Using Skeleton-Based Gait Information](https://mlsanity.com/paper/2201.01115/) (2022)

## Built on by (most cited first, 10 of 10)

- [OpenGait: A Comprehensive Benchmark Study for Gait Recognition towards Better Practicality](https://mlsanity.com/paper/2405.09138/) (2024)
- [The Paradox of Motion: Evidence for Spurious Correlations in Skeleton-based Gait Recognition Models](https://mlsanity.com/paper/2402.08320/) (2024)
- [On Model and Data Scaling for Skeleton-based Self-Supervised Gait Recognition](https://mlsanity.com/paper/2504.07598/) (2025)
- [PsychCounsel-Bench: Evaluating the Psychology Intelligence of Large Language Models](https://mlsanity.com/paper/2510.01611/) (2025)
- [LiCAF: LiDAR-Camera Asymmetric Fusion for Gait Recognition](https://mlsanity.com/paper/2406.12355/) (2024)
- [DepthGait: Multi-Scale Cross-Level Feature Fusion of RGB-Derived Depth and Silhouette Sequences for Robust Gait Recognition](https://mlsanity.com/paper/2508.03397/) (2025)
- [Learning to Simplify Spatial-Temporal Graphs in Gait Analysis](https://mlsanity.com/paper/2310.03396/) (2023)
- [Gait Recognition from Highly Compressed Videos](https://mlsanity.com/paper/2404.12183/) (2024)
- [Database-Agnostic Gait Enrollment using SetTransformers](https://mlsanity.com/paper/2505.02815/) (2025)
- [MoME: Estimating Psychological Traits from Gait with Multi-Stage Mixture of Movement Experts](https://mlsanity.com/paper/2510.04654/) (2025)

## Abstract (arXiv)

Psychological trait estimation from external factors such as movement and appearance is a challenging and long-standing problem in psychology, and is principally based on the psychological theory of embodiment. To date, attempts to tackle this problem have utilized private small-scale datasets with intrusive body-attached sensors. Potential applications of an automated system for psychological trait estimation include estimation of occupational fatigue and psychology, and marketing and advertisement. In this work, we propose PsyMo (Psychological traits from Motion), a novel, multi-purpose and multi-modal dataset for exploring psychological cues manifested in walking patterns. We gathered walking sequences from 312 subjects in 7 different walking variations and 6 camera angles. In conjunction with walking sequences, participants filled in 6 psychological questionnaires, totalling 17 psychometric attributes related to personality, self-esteem, fatigue, aggressiveness and mental health. We propose two evaluation protocols for psychological trait estimation. Alongside the estimation of self-reported psychological traits from gait, the dataset can be used as a drop-in replacement to benchmark methods for gait recognition. We anonymize all cues related to the identity of the subjects and publicly release only silhouettes, 2D / 3D human skeletons and 3D SMPL human meshes.