Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145870
Title: Comparison of wearable and clinical devices for acquisition of peripheral nervous system signals
Authors: Bizzego, Andrea
Gabrieli, Giulio
Furlanello, Cesare
Esposito, Gianluca
Keywords: Social sciences::Psychology
Issue Date: 2020
Source: Bizzego, A., Gabrieli, G., Furlanello, C., & Esposito, G. (2020). Comparison of wearable and clinical devices for acquisition of peripheral nervous system signals. Sensors, 20(23), 6778-. doi:10.3390/s20236778
Project: RG149/16
RT10/19
Journal: Sensors
Abstract: 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. However, the achievable data quality can be lower, and data are 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 six different physiological measures collected from 18 subjects with WDs. This study indicates the need to validate the use of WDs in experimental settings for research and the importance of both technological and signal processing aspects to obtain reliable signals and reproducible results.
URI: https://hdl.handle.net/10356/145870
ISSN: 1424-8220
DOI: 10.3390/s20236778
Rights: © 2020 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SSS Journal Articles

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