ArticleSensors (Basel, Switzerland)2020
Comparison of Wearable and Clinical Devices for Acquisition of Peripheral Nervous System Signals.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Closed-Loop Neuromodulation for Brain Fatigue: From Real-Time Biomarkers to Adaptive Intervention.International journal of molecular sciences · 2026Review
- A lightGBM-based method for the signal quality assessment of wrist photoplethysmography.Physical and engineering sciences in medicine · 2025Article
- Performance Assessment of Heartbeat Detection Algorithms on Photoplethysmograph and Functional NearInfrared Spectroscopy Signals.Sensors (Basel, Switzerland) · 2023Article
- Data quality evaluation in wearable monitoring.Scientific reports · 2022Article
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note.Sensors (Basel, Switzerland) · 2022Review
- Wearable Photoplethysmography for Cardiovascular Monitoring.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2022Article
- Improving the Efficacy of Deep-Learning Models for Heart Beat Detection on Heterogeneous Datasets.Bioengineering (Basel, Switzerland) · 2021Article
- Acquisition and Processing of Brain Signals.Sensors (Basel, Switzerland) · 2021Article
- Preventing and Monitoring Work-Related Diseases in Firefighters: A Literature Review on Sensor-Based Systems and Future Perspectives in Robotic Devices.International journal of environmental research and public health · 2021Review
- Assessing Electrocardiogram and Respiratory Signal Quality of a Wearable Device (SensEcho): Semisupervised Machine Learning-Based Validation Study.JMIR mHealth and uHealth · 2021Article
- The Concept of Advanced Multi-Sensor Monitoring of Human Stress.Sensors (Basel, Switzerland) · 2021Review
- Deep Neural Networks and Transfer Learning on a Multivariate Physiological Signal Dataset.Bioengineering (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.