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

TL;DR

PsyMo is a comprehensive multi-modal dataset capturing gait and psychological traits from 312 subjects, enabling research in psychological trait estimation and gait recognition while ensuring privacy through anonymization.

Contribution

The paper introduces PsyMo, a novel dataset linking gait patterns with psychological traits, facilitating research in psychological assessment and gait recognition with privacy-preserving data.

Findings

Dataset includes 17 psychometric attributes linked to gait.

Two evaluation protocols for psychological trait estimation.

Publicly available anonymized gait data for research.

Abstract

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.