Wearable health devices are ushering in a new age of continuous and
noninvasive remote monitoring. One application of this technology is in anxiety
detection. Many advancements in anxiety detection have happened in controlled
lab settings, but noise prevents these advancements from generalizing to
real-world conditions. We seek to progress the field by studying how noise
impacts model performance and developing models that are robust to noisy,
real-world conditions and, hence, attuned to the commotion of everyday life. In
this study we look to investigate why and how previous methods have failed.
Using the wearable stress and affect detection (WESAD) dataset, we compare the
effect of various intensities of noise on machine learning models classifying
levels of physiological arousal in the three-class classification problem:
baseline vs. stress vs. amusement. Before introducing noise, our baseline model
performance reaches 98.7%, compared to Schmidt 2018's 80.3%. We discuss
potential sources of this discrepancy in results through a careful evaluation
of feature extraction and model architecture choices. Finally, after the
introduction of noise, we provide a thorough analysis of the effect of noise on
each model architecture.

通过比较不同程度的噪音对生理唤醒分类问题分类模型的影响，该研究探讨了为什么和如何以前的方法失败，并分析了噪音对每个模型结构的影响，从而开发出对噪音环境具有鲁棒性的模型，适合于日常生活的喧嚣。