ArticleSensors (Basel, Switzerland)2023
Adaptive Signal-to-Noise Ratio Indicator for Wearable Bioimpedance Monitoring.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- High-Sensitivity Bio-Waste-Derived Triboelectric Sensors for Capturing Pathological Motor Features in Hemiplegia Rehabilitation.Micromachines · 2026Article
- Smartphone-assisted portable immunosensor toward on-site detection of tumor necrosis factor-α inhibitor adalimumab for personalized treatment.Mikrochimica acta · 2025Article
- Advances in Portable and Wearable Acoustic Sensing Devices for Human Health Monitoring.Sensors (Basel, Switzerland) · 2024Review
- The Use of Electrocardiogram Smartwatches in Patients with Cardiac Implantable Electrical Devices.Sensors (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Bioimpedance monitoring is an increasingly important non-invasive technique for assessing physiological parameters such as body composition, hydration levels, heart rate, and breathing. However, sensor signals obtained from real-world experimental conditions invariably contain noise, which can significantly degrade the reliability of the derived quantities. Therefore, it is crucial to evaluate the quality of measured signals to ensure accurate physiological parameter values. In this study, we present a novel wrist-worn wearable device for bioimpedance monitoring, and propose a method for estimating signal quality for sensor signals obtained on the device. The method is based on the continuous wavelet transform of the measured signal, identification of wavelet ridges, and assessment of their energy weighted by the ridge duration. We validate the algorithm using a small-scale experimental study with the wearable device, and explore the effects of variables such as window size and different skin/electrode coupling agents on signal quality and repeatability. In comparison with traditional wavelet-based signal denoising, the proposed method is more adaptive and achieves a comparable signal-to-noise ratio.
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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.