ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2022
Quantitative and Real-Time Evaluation of Human Respiration Signals with a Shape-Conformal Wireless Sensing System.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 39 citations in OpenAlex.
- Smart Face Masks as Wearable Respiratory Sensors: A Review of Sensor Technologies, Materials, and Future Directions.Advanced healthcare materials · 2026Review
- Artificial Intelligence-Driven Soft Bioelectronics for Self-Powered Respiration Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Direct extraction of respiratory information from pulse waves using a finger-inspired flexible pressure sensor system.Microsystems & nanoengineering · 2025Article
- RespirAnalyzer: an R package for analyzing data from continuous monitoring of respiratory signals.Bioinformatics advances · 2024Article
- Wireless, Smart Hemostasis Device with All-Soft Sensing System for Quantitative and Real-Time Pressure Evaluation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Miniaturized retractable thin-film sensor for wearable multifunctional respiratory monitoring.Nano research · 2023Article
- Identification of featured necroptosis-related genes and imbalanced immune infiltration in sepsisFrontiers in genetics · 2023Article
- Quantitative and Real-Time Evaluation of Human Respiration Signals with a Shape-Conformal Wireless Sensing System.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Wearable respiratory sensors for COVID-19 monitoring.View (Beijing, China) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 6 institutions in 3 countries.
Funding
Abstract
Respiration signals reflect many underlying health conditions, including cardiopulmonary functions, autonomic disorders and respiratory distress, therefore continuous measurement of respiration is needed in various cases. Unfortunately, there is still a lack of effective portable electronic devices that meet the demands for medical and daily respiration monitoring. This work showcases a soft, wireless, and non-invasive device for quantitative and real-time evaluation of human respiration. This device simultaneously captures respiration and temperature signatures using customized capacitive and resistive sensors, encapsulated by a breathable layer, and does not limit the user's daily life. Further a machine learning-based respiration classification algorithm with a set of carefully studied features as inputs is proposed and it is deployed into mobile clients. The body status of users, such as being quiet, active and coughing, can be accurately recognized by the algorithm and displayed on clients. Moreover, multiple devices can be linked to a server network to monitor a group of users and provide each user with the statistical duration of physiological activities, coughing alerts, and body health advice. With these devices, individual and group respiratory health status can be quantitatively collected, analyzed, and stored for daily physiological signal detections as well as medical assistance.
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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.