A key access point to the functioning of the Autonomic Nervous System is the investigation of peripheral signals. Wearable Devices (WDs) enable the acquisition and quantification of peripheral signals in a wide range of contexts, from personal uses to scientific research. WDs have lower costs and higher portability than medical-grade devices. But achievable data quality can be lower, subject to artifacts due to body movements and data losses. It is therefore crucial to evaluate the reliability and validity of WDs before their use in research. In this study we introduce a data analysis procedure for the assessment of WDs for multivariate physiological signals. The quality of cardiac and Electrodermal Activity signals is validated with a standard set of Signal Quality Indicators. The pipeline is available as a collection of open source Python scripts based on the pyphysio package. We apply the indicators for the analysis of signal quality on data simultaneously recorded from a clinical-grade device and two WDs. The dataset provides signals of 6 different physiological measures collected from 18 subjects with WDs. This study indicates the need of validating the use of WD in experimental settings for research and the importance of both technological and signal processing aspects to obtain reliable signals and reproducibility of results.

中文翻译：

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可穿戴设备和临床设备用于采集周围神经系统信号的比较

自主神经系统功能的关键访问点是对外围信号的研究。从个人使用到科学研究，可穿戴设备（WD）可以在各种环境中采集和量化外围信号。与医疗级设备相比，WD具有更低的成本和更高的便携性。但是，由于人体运动和数据丢失，可达到的数据质量可能会降低，从而受到伪影的影响。因此，在将WD用于研究之前，评估其可靠性和有效性至关重要。在这项研究中，我们介绍了一种用于评估多变量生理信号的WD的数据分析程序。心脏和皮肤电活动信号的质量通过一套标准的信号质量指标进行验证。该管道可作为基于pyphysio软件包的开源Python脚本的集合使用。我们将指标用于对从临床级设备和两个WD同时记录的数据进行信号质量分析。该数据集提供了从18名WD患者中收集的6种不同生理指标的信号。这项研究表明需要在实验的实验环境中验证WD的使用，以及技术和信号处理方面对于获得可靠信号和结果可重复性的重要性。