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Stress Recognition with State Classification Considering Temporal Variation of Stress Responses

机译：考虑状态响应时间变化的状态分类应力识别

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摘要

To avoid and manage stress-related problems, several works have investigated how to recognize stress states of people by exploiting the machine learning. Most of works utilize the supervised learning which requires labels for data to be trained and tested. Thus, labels which represent stress state of each data such as stressed or not become definitely important. Conventional stress state classification methods assign labels to unit data per a certain experimental period by applying stressor appearances or self-evaluation scores. However, those methods ignore temporal variations in stress responses which are involuntarily triggered inside the body within a shorter period of time than an experimental period. Therefore, we propose a stress state classification method by considering not only user's subjective evaluations but also temporal changes of stress responses in short periods. For the demonstration, we label our experimental data of 40 subjects by using our proposed classification method and conventional ones, respectively. Then, we train and test stress recognition models with 6 machine learning algorithms and our implemented neural network ones based on the labeled data. Finally, binary stress recognition with our proposed classification method improves the recognition accuracy by up to 31.6% as compared to those with conventional techniques.
机译：为了避免和管理与压力有关的问题，一些工作研究了如何通过利用机器学习来识别人的压力状态。大多数作品利用监督学习，这需要对数据进行培训和测试的标签。因此，表示每个数据的应力状态（例如应力或不应力）的标签变得绝对重要。常规压力状态分类方法是通过应用压力源外观或自我评估得分，在每个实验周期内将标签分配给单位数据。但是，这些方法忽略了应力响应的时间变化，该应力变化是在比实验周期短的时间内在体内非自愿触发的。因此，我们提出一种压力状态分类方法，该方法不仅要考虑用户的主观评价，还要考虑短期内压力响应的时间变化。为了进行演示，我们分别使用我们提出的分类方法和常规方法对40个受试者的实验数据进行标记。然后，我们使用6种机器学习算法和基于标记数据的已实现的神经网络算法训练和测试压力识别模型。最后，与传统技术相比，使用我们提出的分类方法进行的二元应力识别将识别精度提高了31.6％。

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