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.
Abstract (translated)
心理特征从外部因素如运动和外观进行评估是一个挑战性的长期问题,主要基于身体存在感的心理理论。迄今为止,尝试解决这个问题的方法使用了具有内置身体传感器的私人小型数据集。自动化系统的心理特征估计潜在应用包括衡量工作疲劳和心理学,以及市场营销和广告。在这个工作中,我们提出了 PsyMo (心理特征从运动),一个新颖、多功能和多模态的数据集,以探索步行模式中的心理迹象。我们从312名 subjects 收集了7种不同的步行变化和6个摄像头角度的步行序列。与步行序列一起,参与者填写了6个心理问卷,总共涉及17个心理属性,与个性、自尊心、疲劳、攻击性和心理健康相关。我们提出了两个评估协议,以心理特征估计为主。除了从步态中自我报告的心理特征估计外,该数据集还可以作为步态识别基准方法的备用方法。我们匿名化与 subjects 身份相关的所有线索,并仅公开发布轮廓、2D/3D人类骨骼模型和3D SMPL人类网格。
URL
https://arxiv.org/abs/2308.10631
