消费级可穿戴设备上应力检测模型的性能分析
Analysing the Performance of Stress Detection Models on Consumer-Grade Wearable Devices
摘要：
识别压力水平可以为心理健康分析提供有价值的数据，并为注释系统提供标签。尽管已经以较高的数据汇集成本对使用心率变异性的压力检测模型进行了大量研究，但是缺乏对使用来自消费级可穿戴设备的低分辨率皮肤电活动（EDA）信号来识别压力模式的潜力的研究。在本文中，我们专注于进行统计分析的压力检测能力的两种流行的方法训练压力检测模型与压力相关的生物特征信号：用户相关和用户无关的模型。我们的研究设法表明，用户相关的模型在统计上更准确地检测压力。在有效性评估方面，采用平衡精度（BA）度量来评估在低分辨率或高分辨率皮肤电活动（EDA）信号上训练的模型区分应力和非应力条件的能力。实验结果表明，与使用高分辨率EDA信号相比，使用（相对低成本的）低分辨率EDA信号训练模型不会显著影响模型的应力检测精度。我们的研究结果表明，附加在个人低分辨率EDA信号上训练的依赖于用户的压力检测模型可以收集日常生活中的数据，为用户提供个人压力水平的洞察和分析。
英文摘要：
Identifying stress levels can provide valuable data for mental health analytics as well as labels for annotation systems. Although much research has been conducted into stress detection models using heart rate variability at a higher cost of data collection, there is a lack of research on the potential of using low-resolution Electrodermal Activity (EDA) signals from consumer-grade wearable devices to identify stress patterns. In this paper, we concentrate on performing statistical analyses on the stress detection capability of two popular approaches of training stress detection models with stress-related biometric signals: user-dependent and user-independent models. Our research manages to show that user-dependent models are statistically more accurate for stress detection. In terms of effectiveness assessment, the balanced accuracy (BA) metric is employed to evaluate the capability of distinguishing stress and non-stress conditions of the models trained on either low-resolution or high-resolution Electrodermal Activity (EDA) signals. The results from the experiment show that training the model with (comparatively low-cost) low-resolution EDA signal does not affect the stress detection accuracy of the model significantly compared to using a high-resolution EDA signal. Our research results demonstrate the potential of attaching the user-dependent stress detection model trained on personal low-resolution EDA signal recorded to collect data in daily life to provide users with personal stress level insight and analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
