ArticleIEEE transactions on bio-medical engineering2022
Objective Scoring of Physiologically Induced Dyspnea by Non-Invasive RF Sensors.
Article in IEEE transactions on bio-medical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 14 citations in OpenAlex.
- Objective dyspnea measurement in real time using a forehead wearable and artificial intelligence.NPJ primary care respiratory medicine · 2026Article
- Forearm Radiomyography for Character and Writer Identification in Air Writing.IEEE journal of electromagnetics, RF and microwaves in medicine and biology · 2026Article
- A Near-Field Radio Frequency System for Continuous Left and Right Lung Volume Sensing.IEEE sensors journal · 2025Article
- Novel Muscle Sensing by Radiomyography (RMG) and Its Application to Hand Gesture Recognition.IEEE sensors journal · 2023Article
- Deduced Respiratory Scores on COVID-19 Patients Learning from Exertion-Induced Dyspnea.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
objectiveDyspnea, also known as the patient's feeling of difficult or labored breathing, is one of the most common symptoms for respiratory disorders. Dyspnea is usually self-reported by patients using, for example, the Borg scale from 0 - 10, which is however subjective and problematic for those who refuse to cooperate or cannot communicate. The objective of this paper was to develop a learning-based model that can evaluate the correlation between the self-report Borg score and the respiratory metrics for dyspnea induced by exertion and increased airway resistance.
methodsA non-invasive wearable radio-frequency sensor by near-field coherent sensing was employed to retrieve continuous respiratory data with user comfort and convenience. Self-report dyspnea scores and respiratory features were collected on 32 healthy participants going through various physical and breathing exercises. A machine learning model based on the decision tree and random forest then produced an objective dyspnea score.
resultsFor unseen data as well as unseen participants, the objective dyspnea score can be in reasonable agreement with the self-report score, and the importance factor of each respiratory metrics can be assessed.
conclusionAn objective dyspnea score can potentially complement or substitute the self-report for physiologically induced dyspnea. SIGNIFICANCE: The method can potentially formulate a baseline for clinical dyspnea assessment and help caregivers track dyspnea continuously, especially for patients who cannot report themselves.
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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.