Sport and Exercise Psychology, University of Potsdam, Potsdam, Germany.
PLoS One. 2020 Feb 11;15(2):e0228739. doi: 10.1371/journal.pone.0228739. eCollection 2020.
Recent research indicates that affective responses during exercise are an important determinant of future exercise and physical activity. Thus far these responses have been measured with standardized self-report scales, but this study used biometric software for automated facial action analysis to analyze the changes that occur during physical exercise. A sample of 132 young, healthy individuals performed an incremental test on a cycle ergometer. During that test the participants' faces were video-recorded and the changes were algorithmically analyzed at frame rate (30 fps). Perceived exertion and affective valence were measured every two minutes with established psychometric scales. Taking into account anticipated inter-individual variability, multilevel regression analysis was used to model how affective valence and ratings of perceived exertion (RPE) covaried with movement in 20 facial action areas. We found the expected quadratic decline in self-reported affective valence (more negative) as exercise intensity increased. Repeated measures correlation showed that the facial action mouth open was linked to changes in (highly intercorrelated) affective valence and RPE. Multilevel trend analyses were calculated to investigate whether facial actions were typically linked to either affective valence or RPE. These analyses showed that mouth open and jaw drop predicted RPE, whereas (additional) nose wrinkle was indicative for the decline in affective valence. Our results contribute to the view that negative affect, escalating with increasing exercise intensity, may be the body's essential warning signal that physiological overload is imminent. We conclude that automated facial action analysis provides new options for researchers investigating feelings during exercise. In addition, our findings offer physical educators and coaches a new way of monitoring the affective state of exercisers, without interrupting and asking them.
最近的研究表明，运动过程中的情感反应是未来运动和体育活动的一个重要决定因素。到目前为止，这些反应都是通过标准化的自我报告量表来测量的，但本研究使用生物识别软件进行自动面部动作分析，以分析体育锻炼过程中发生的变化。一个由 132 名年轻健康的个体组成的样本在自行车测功计上进行了递增测试。在测试过程中，参与者的面部被录像记录下来，并以每秒 30 帧的帧率进行算法分析。使用既定的心理测量量表每两分钟测量一次感知用力和情感效价。考虑到预期的个体间变异性，多级回归分析用于对情感效价和感知用力（RPE）评分与 20 个面部动作区域的运动如何协变进行建模。我们发现，随着运动强度的增加，自我报告的情感效价（更负面）呈预期的二次下降。重复测量相关显示，口部张开的面部动作与（高度相关）情感效价和 RPE 的变化有关。进行多层次趋势分析，以研究面部动作是否通常与情感效价或 RPE 有关。这些分析表明，口部张开和下颚下垂预测 RPE，而（额外的）鼻皱纹则表明情感效价下降。我们的研究结果有助于这样一种观点，即随着运动强度的增加而加剧的负性情绪可能是身体即将面临生理过载的基本警告信号。我们得出的结论是，自动面部动作分析为研究运动中情感的研究人员提供了新的选择。此外，我们的研究结果为体育教育者和教练提供了一种新的方法来监测锻炼者的情感状态，而不会中断并询问他们。
