Liu Xingyun, Wen Yeye, Zhu Tingshao
Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, School of Psychology, Central China Normal University, Wuhan, China.
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
Front Psychiatry. 2022 Oct 10;13:1027445. doi: 10.3389/fpsyt.2022.1027445. eCollection 2022.
Self-esteem is a significant kind of psychological resource, and behavioral self-esteem assessments are rare currently. Using ordinary cameras to capture one's gait pattern to reveal people's self-esteem meets the requirement for real-time population-based assessment. A total of 152 healthy students who had no walking issues were recruited as participants. The self-esteem scores and gait data were obtained using a standard 2D camera and the Rosenberg Self-Esteem Scale (RSES). After data preprocessing, dynamic gait features were extracted for training machine learning models that predicted self-esteem scores based on the data. For self-esteem prediction, the best results were achieved by Gaussian processes and linear regression, with a correlation of 0.51 ( < 0.001), 0.52 ( < 0.001), 0.46 ( < 0.001) for all participants, males, and females, respectively. Moreover, the highest reliability was 0.92 which was achieved by RBF-support vector regression. Gait acquired by a 2D camera can predict one's self-esteem quite well. This innovative approach is a good supplement to the existing methods in ecological recognition of self-esteem leveraged by video-based gait.
自尊是一种重要的心理资源，而目前行为自尊评估较为少见。使用普通相机捕捉一个人的步态模式来揭示其自尊符合基于人群的实时评估要求。总共招募了152名没有行走问题的健康学生作为参与者。使用标准二维相机和罗森伯格自尊量表（RSES）获取自尊分数和步态数据。经过数据预处理后，提取动态步态特征以训练基于数据预测自尊分数的机器学习模型。对于自尊预测，高斯过程和线性回归取得了最佳结果，所有参与者、男性和女性的相关系数分别为0.51（<0.001）、0.52（<0.001）、0.46（<0.001）。此外，径向基函数支持向量回归实现的最高可靠性为0.92。二维相机获取的步态能够很好地预测一个人的自尊。这种创新方法是对基于视频步态的自尊生态识别现有方法的良好补充。
