Respiratory Rate Estimation by Using ECG, Impedance, and Motion Sensing in Smart Clothing

Shen, Chien Lung; Huang, Tzu Hao; Hsu, Ping-Chi; Ko, Ya Chi; Chen, Fen Ling; Wang, Wei Chun; Kao, Tsair; Chan, Chuan-Tsung

doi:10.1007/s40846-017-0247-z

Cited by 59 publications

(50 citation statements)

References 38 publications

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“…When a gyroscope was used in conjunction with a triaxial accelerometer on healthy subjects in stationary conditions, the average values of fR showed discrepancies always lower than 1.5 bpm (average error of 0.77 bpm), when compared to values manually counted by the authors [222]. Respiratory patterns obtained fusing accelerometer and gyroscope signals were investigated in [225]: the average error in estimating average fR values was 0.7 bpm during QB (max value of 2.0 bpm), and 3.0 bpm during running (max value of 4.0 bpm) [225].…”

Section: Techniques Based On Chest Wall Movement Analysismentioning

confidence: 99%

Contact-Based Methods for Measuring Respiratory Rate

Massaroni

Nicolò

Presti

et al. 2019

Sensors

324

236

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There is an ever-growing demand for measuring respiratory variables during a variety of applications, including monitoring in clinical and occupational settings, and during sporting activities and exercise. Special attention is devoted to the monitoring of respiratory rate because it is a vital sign, which responds to a variety of stressors. There are different methods for measuring respiratory rate, which can be classed as contact-based or contactless. The present paper provides an overview of the currently available contact-based methods for measuring respiratory rate. For these methods, the sensing element (or part of the instrument containing it) is attached to the subject’s body. Methods based upon the recording of respiratory airflow, sounds, air temperature, air humidity, air components, chest wall movements, and modulation of the cardiac activity are presented. Working principles, metrological characteristics, and applications in the respiratory monitoring field are presented to explore potential development and applicability for each method.

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Section: Techniques Based On Chest Wall Movement Analysismentioning

confidence: 99%

Contact-Based Methods for Measuring Respiratory Rate

Massaroni

Nicolò

Presti

et al. 2019

Sensors

324

236

View full text Add to dashboard Cite

show abstract

“…The traditional automatic measurement of RF is contact-based, which requires big devices such as the spirometry, capnotmetry, and impedance pneumography (Shen et al., 2017). In recent years, many innovative devices have been attempted to continuously measure RF in non-contact ways.…”

Section: Introductionmentioning

confidence: 99%

Toward Accurate Extraction of Respiratory Frequency From the Photoplethysmogram: Effect of Measurement Site

et al. 2019

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Background: It is known that the respiration-modulated photoplethysmographic (PPG) signals could be used to derive respiratory frequency (RF) and that PPG signals could be measured from different body sites. However, the accuracy of RF derived from PPG signals of different body sites has not been comprehensively investigated. Objective: This study aims to investigate the difference in the accuracy of PPG-derived RFs between measurements from different body sites, respectively, for normal and deep breathing conditions. Methods: Under normal and deep breathing patterns, the PPG signals were recorded sequentially in a randomized order from six body sites [finger, wrist under (anatomically volar), wrist upper (dorsal), earlobe, and forehead] of 36 healthy subjects. Simultaneously, the reference respiratory signal was measured by a respiratory belt on the chest. Using the frequency demodulation approach, respiratory signals were extracted from PPG signals for calculating RF by power spectral density. The bias between PPG-derived and reference RFs was then analyzed statistically using analysis of variance and non-parametric tests, Bland-Altman analysis, and linear regression to investigate the difference in RF bias between different sites. Results: The RF bias was significantly influenced by the breathing pattern and measurement site (both p < 0.001). Under normal breathing, the RF bias was insignificant in the arm, forehead, and wrist under (all p > 0.05) and significant in the other sites (all p < 0.05). Significant linear relationship between PPG-derived and reference RFs existed at all the sites ( p < 0.001) except the wrist upper ( p > 0.05). The linearity between PPG-derived and reference RFs was highest at the forehead (slope of best-fit line: 0.90, R 2 : 0.64), followed by the earlobe, finger, arm, and wrist under (slope: 0.71, R 2 : 0.40). Under deep breathing, there was no significant RF bias in all the measurement sites ( p > 0.05) except forehead ( p = 0.048). The effect of measurement site on RF bias was not significant ( p > 0.05). The finger had the smallest RF bias and the narrowest limits of agreement. Conclusion: This study has demonstrated that the accuracy of PPG-derived RF depends on the measurement site and breathing pattern. The best sites are the forehead and finger, respectively, for normal and deep breathing patterns.

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“…The ANOVA results showed that IJP sensor had high accuracy, good repeatability, and stability at different body postures. With no observable deviation from the reference sensor at all body postures, the accuracy of the IJP sensor was better than many sensors reported in the literature where the RR measured by stretchable sensor deviated from the reference value from 1.7% to more than 6% in different postures [4,12,[26][27][28][29][30].…”

Section: Ijp Sensor Accuracy: Comparison With Other Sensorsmentioning

confidence: 68%

Clinical Evaluation of Stretchable and Wearable Inkjet-Printed Strain Gauge Sensor for Respiratory Rate Monitoring at Different Body Postures

et al. 2020

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Respiratory rate (RR) is a vital sign with continuous, convenient, and accurate measurement which is difficult and still under investigation. The present study investigates and evaluates a stretchable and wearable inkjet-printed strain gauge sensor (IJP) to estimate the RR continuously by detecting the respiratory volume change in the chest area. As the volume change could cause different strain changes at different body postures, this study aims to investigate the accuracy of the IJP RR sensor at selected postures. The evaluation was performed twice on 15 healthy male subjects (mean ± SD of age: 24 ± 1.22 years). The RR was simultaneously measured in breaths per minute (BPM) by the IJP RR sensor and a reference RR sensor (e-Health nasal thermal sensor) at each of the five body postures namely standing, sitting at 90°, Flower’s position at 45°, supine, and right lateral recumbent. There was no significant difference in measured RR between IJP and reference sensors, between two trials, or between different body postures (all p > 0.05). Body posture did not have any significant effect on the difference of RR measurements between IJP and the reference sensors (difference <0.01 BPM for each measurement in both trials). The IJP sensor could accurately measure the RR at different body postures, which makes it a promising, simple, and user-friendly option for clinical and daily uses.

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Respiratory Rate Estimation by Using ECG, Impedance, and Motion Sensing in Smart Clothing

Cited by 59 publications

References 38 publications

Contact-Based Methods for Measuring Respiratory Rate

Contact-Based Methods for Measuring Respiratory Rate

Toward Accurate Extraction of Respiratory Frequency From the Photoplethysmogram: Effect of Measurement Site

Clinical Evaluation of Stretchable and Wearable Inkjet-Printed Strain Gauge Sensor for Respiratory Rate Monitoring at Different Body Postures

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