Comparison of wearable and clinical devices for acquisition of peripheral nervous system signals

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 hi...

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Main Authors: Bizzego, Andrea, Gabrieli, Giulio, Furlanello, Cesare, Esposito, Gianluca
Other Authors: School of Social Sciences
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/145870
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1458702023-03-05T15:31:11Z Comparison of wearable and clinical devices for acquisition of peripheral nervous system signals Bizzego, Andrea Gabrieli, Giulio Furlanello, Cesare Esposito, Gianluca School of Social Sciences Lee Kong Chian School of Medicine (LKCMedicine) Social sciences::Psychology Wearable Devices Physiological Data Analysis 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. Ministry of Education (MOE) Published version A.B. was supported by a Post-Doctoral Fellowship within the MIUR program framework “Dipartimenti di Eccellenza” (DiPSCO, University of Trento). G.E. was supported by NAP SUG 2015, Singapore Ministry of Education ACR Tier 1 (RG149/16 and RT10/19). 2021-01-13T02:30:00Z 2021-01-13T02:30:00Z 2020 Journal Article 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 1424-8220 https://hdl.handle.net/10356/145870 10.3390/s20236778 33260880 2-s2.0-85096705052 23 20 en RG149/16 RT10/19 Sensors © 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/). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Social sciences::Psychology
Wearable Devices
Physiological Data Analysis
spellingShingle Social sciences::Psychology
Wearable Devices
Physiological Data Analysis
Bizzego, Andrea
Gabrieli, Giulio
Furlanello, Cesare
Esposito, Gianluca
Comparison of wearable and clinical devices for acquisition of peripheral nervous system signals
description 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.
author2 School of Social Sciences
author_facet School of Social Sciences
Bizzego, Andrea
Gabrieli, Giulio
Furlanello, Cesare
Esposito, Gianluca
format Article
author Bizzego, Andrea
Gabrieli, Giulio
Furlanello, Cesare
Esposito, Gianluca
author_sort Bizzego, Andrea
title Comparison of wearable and clinical devices for acquisition of peripheral nervous system signals
title_short Comparison of wearable and clinical devices for acquisition of peripheral nervous system signals
title_full Comparison of wearable and clinical devices for acquisition of peripheral nervous system signals
title_fullStr Comparison of wearable and clinical devices for acquisition of peripheral nervous system signals
title_full_unstemmed Comparison of wearable and clinical devices for acquisition of peripheral nervous system signals
title_sort comparison of wearable and clinical devices for acquisition of peripheral nervous system signals
publishDate 2021
url https://hdl.handle.net/10356/145870
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