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.

翻译：可穿戴健康设备正引领持续、非侵入式远程监控的新时代。该技术的一项应用是焦虑检测。尽管焦虑检测在受控实验室环境中取得了诸多进展，但噪声阻碍了这些成果推广至真实世界条件。我们旨在通过研究噪声如何影响模型性能，并开发对嘈杂真实环境具有鲁棒性、从而适配日常生活喧嚣的模型来推动该领域发展。本研究着力探究过往方法失败的原因与机制。利用可穿戴压力与情绪检测（WESAD）数据集，我们比较了不同强度噪声对机器学习模型在三分类问题（基准状态 vs. 应激状态 vs. 娱乐状态）中生理唤醒水平分类性能的影响。在引入噪声前，我们的基线模型性能达到98.7%，而Schmidt 2018年的结果为80.3%。通过对特征提取和模型架构选择的细致评估，我们讨论了这一结果差异的潜在来源。最终，在引入噪声后，我们系统分析了噪声对每种模型架构的影响。