论文标题
来自具有神经过程的生理信号的个性化急性压力分类
Personalized acute stress classification from physiological signals with neural processes
论文作者
论文摘要
目的：一个人的情感状态与可以通过可穿戴传感器来衡量的生理过程已经知道了关系。但是，尽管存在一般趋势，但这些关系可能是特定于人的。这项工作建议使用神经过程来解决个体差异。 方法：使用经典的机器学习模型和神经过程构建的压力分类器在两个数据集上使用剩下的参与者交叉验证进行比较。神经过程模型在特定人录制的短时间内的数据上进行了背景群体。 结果：神经过程模型的表现优于标准机器学习模型，并且在使用压力期和基线作为背景时的表现最好。从其他参与者中选择的上下文点导致较低的性能。 结论：神经过程可以学会适应特定于人的生理传感器数据。该模型可以证明有用的多种情感和医疗应用。
Objective: A person's affective state has known relationships to physiological processes which can be measured by wearable sensors. However, while there are general trends those relationships can be person-specific. This work proposes using neural processes as a way to address individual differences. Methods: Stress classifiers built from classic machine learning models and from neural processes are compared on two datasets using leave-one-participant-out cross-validation. The neural processes models are contextualized on data from a brief period of a particular person's recording. Results: The neural processes models outperformed the standard machine learning models, and had the best performance when using periods of stress and baseline as context. Contextual points chosen from other participants led to lower performance. Conclusion: Neural processes can learn to adapt to person-specific physiological sensor data. There are a wide range of affective and medical applications for which this model could prove useful.
