Comparison of Wearable and Clinical Devices for Acquisition of Peripheral Nervous System Signals
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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.
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Signal quality assessments have been done under more general circumstances, but wearable data quality in the context of epilepsy has not been a widely researched topic so far.
“Signal quality assessments have been done under more general circumstances10,34–37, but wearable data quality in the context of epilepsy has not been a widely researched topic so far8,9,38.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [36]
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References 55 · Cited by 25
Research works printed but not linked: 1
- [23]Yadhuraj S. Harsha H. Motion Artifact Reduction in Photoplethysmographic Signals: A Review Int. J. Innov. Res. Dev. 2013 2 626 640
- Authors
- Bizzego, A., Gabrieli, G., Furlanello, C., & Esposito, G.
- Affiliations
- University of Trento (IT)
- Nanyang Technological University (SG)
- HK3 Lab, Rovereto, 38068 Trento, Italy
- Corresponding author
- Esposito, Gianluca
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-11-27
- Volume
- 20
- Issue
- 23
- Pages
- 6778
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7730565
- OpenAlex
- W3108311659
- Cited by
- 25
- References
- 55
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 2 · 2023: 1 · 2022: 8 · 2021: 9
- FWCI
- 1.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Institutional Research Information System (Università degli Studi di Trento) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DR-NTU (Nanyang Technological University) (submittedVersion, cc-by)
- Funders
- Ministero dell'Istruzione, dell'Università e della Ricerca (Dipartimenti di Eccellenza)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- wearable devices, autonomic nervous system, electrodermal activity, cardiac signals, signal quality assessment, signal quality indicators, device validation, body movement artifacts
- MeSH terms
- Wearable Electronic Devices, Autonomic Nervous System, Humans, Male, Signal Processing, Computer-Assisted, Reproducibility of Results, Artifacts
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