Department of Applied Physiology & Kinesiology, University of Florida, Gainesville, FL, USA; Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
Institute of Psychology, Chinese Academy of Sciences, Beijing, China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China.
Gait Posture. 2024 Mar;109:15-21. doi: 10.1016/j.gaitpost.2024.01.013. Epub 2024 Jan 15.
Stress is a critical risk factor for various health issues, but an objective, non-intrusive and effective measurement approach for stress has not yet been established. Gait, the pattern of movements in human locomotion, has been proven to be a valid behavioral indicator for recognizing various mental states in a convenient manner.
This study aims to identify the severity of stress by assessing human gait recorded through an objective, non-intrusive measurement approach.
One hundred and fifty-two participants with an average age of 23 years old (SD = 1.07) were recruited. The Chinese version of the Perceived Stress Scale with 10 items (PSS-10) was used to assess participants' stress levels. The participants were then required to walk naturally while being recorded with a regular camera. A total of 1320 time-domain and 1152 frequency-domain gait features were extracted from the videos. The top 40 contributing features, confirmed by dimensionality reduction, were input into models consisting of four machine-learning regression algorithms (i.e., Gaussian Process Regressor, Linear Regression, Random Forest Regressor, and Support Vector regression), to assess stress levels.
The models that combined time- and frequency-domain features performed best, with the lowest RMSE (4.972) and highest validation (r = 0.533). The Gaussian Process Regressor and Linear Regression outperformed the others. The greatest contribution to model performance was derived from gait features of the waist, hands, and legs.
The severity of stress can be accurately detected by machine learning models using two-dimensional (2D) video-based gait data. The machine learning models used for assessing perceived stress were reliable. Waist, hand, and leg movements were found to be critical indicator in detecting stress.
压力是各种健康问题的关键风险因素，但尚未建立客观、非侵入性和有效的压力测量方法。步态，即人类运动的模式，已被证明是一种有效的行为指标，可以方便地识别各种心理状态。
本研究旨在通过评估通过客观、非侵入性测量方法记录的人类步态来确定压力的严重程度。
招募了 152 名平均年龄为 23 岁（SD=1.07）的参与者。使用 10 项感知压力量表（PSS-10）评估参与者的压力水平。然后要求参与者在被常规相机记录的同时自然行走。从视频中提取了 1320 个时域和 1152 个频域步态特征。通过降维确认了前 40 个贡献特征，并将其输入到由四个机器学习回归算法（即高斯过程回归器、线性回归、随机森林回归器和支持向量回归器）组成的模型中，以评估压力水平。
结合时频域特征的模型表现最佳，RMSE（4.972）最低，验证值（r=0.533）最高。高斯过程回归器和线性回归器的表现优于其他模型。模型性能的最大贡献来自腰部、手部和腿部的步态特征。
使用二维（2D）基于视频的步态数据，机器学习模型可以准确检测压力的严重程度。用于评估感知压力的机器学习模型是可靠的。腰部、手部和腿部运动被发现是检测压力的关键指标。
