Recent development of respiratory rate measurement technologies

Liu, Haipeng; Allen, John; Zheng, Dingchang; Chen, Fei

doi:10.1088/1361-6579/ab299e

Cited by 212 publications

(164 citation statements)

References 225 publications

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“…All above mentioned methods are explained in detail in [119]. Another very detailed review focused on respiration measurement methods, sensors, and signal processing techniques is provided in [120].…”

Section: Respiration Rate Analysis (Rr)mentioning

confidence: 99%

Human Emotion Recognition: Review of Sensors and Methods

Dzedzickis

Kaklauskas

Bučinskas

2020

Sensors

406

170

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Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or human–robot interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems.

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Section: Respiration Rate Analysis (Rr)mentioning

confidence: 99%

Human Emotion Recognition: Review of Sensors and Methods

Dzedzickis

Kaklauskas

Bučinskas

2020

Sensors

406

170

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“…Besides ECG and PPG based methods, several additional concepts of estimating respiratory parameters have emerged. Other approaches used to estimate respiratory signals include respiratory sounds, breathing air temperature, humidity and components [37], the oscillometric cuff pressure, Korotkoff sounds, as well as the seismocardiogram (SCG) [38]. For the latter, it has been shown that the morphology of the vibrational cardiac waveforms varies with the respiratory volume [39,40], and methods of extracting respiratory phases [41,42], effort [43] and breathing states such as normal, breathless, long, and labored [44] have been proposed.…”

Section: Clinical Background and State-of-the-artmentioning

confidence: 99%

Wearable Cardiorespiratory Monitoring Employing a Multimodal Digital Patch Stethoscope: Estimation of ECG, PEP, LVET and Respiration Using a 55 mm Single-Lead ECG and Phonocardiogram

Klum

Tigges

Pielmuş

et al. 2020

Sensors

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Cardiovascular diseases are the main cause of death worldwide, with sleep disordered breathing being a further aggravating factor. Respiratory illnesses are the third leading cause of death amongst the noncommunicable diseases. The current COVID-19 pandemic, however, also highlights the impact of communicable respiratory syndromes. In the clinical routine, prolonged postanesthetic respiratory instability worsens the patient outcome. Even though early and continuous, long-term cardiorespiratory monitoring has been proposed or even proven to be beneficial in several situations, implementations thereof are sparse. We employed our recently presented, multimodal patch stethoscope to estimate Einthoven electrocardiogram (ECG) Lead I and II from a single 55 mm ECG lead. Using the stethoscope and ECG subsystems, the pre-ejection period (PEP) and left ventricular ejection time (LVET) were estimated. ECG-derived respiration techniques were used in conjunction with a novel, phonocardiogram-derived respiration approach to extract respiratory parameters. Medical-grade references were the SOMNOmedics SOMNO HD TM and Osypka ICON-Core TM . In a study including 10 healthy subjects, we analyzed the performances in the supine, lateral, and prone position. Einthoven I and II estimations yielded correlations exceeding 0.97. LVET and PEP estimation errors were 10% and 21%, respectively. Respiratory rates were estimated with mean absolute errors below 1.2 bpm, and the respiratory signal yielded a correlation of 0.66. We conclude that the estimation of ECG, PEP, LVET, and respiratory parameters is feasible using a wearable, multimodal acquisition device and encourage further research in multimodal signal fusion for respiratory signal estimation.

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“…Some researchers extract RR from other physiological signals such as photoplethysmogram and electrocardiogram [11]. Other studies derived respiration signals using non-contact techniques such as C-band sensing [12] and dual smartphone cameras [4].…”

Section: Introductionmentioning

confidence: 99%

“…Other studies derived respiration signals using non-contact techniques such as C-band sensing [12] and dual smartphone cameras [4]. Some researchers tried distant (less than 10 m) RR detecting based on Wi-Fi, radar, or thermal imaging [11]. Compared with these techniques, wearable and stretchable sensors could independently measure RR without limiting the daily activities of the users.…”

Section: Introductionmentioning

confidence: 99%

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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Recent development of respiratory rate measurement technologies

Cited by 212 publications

References 225 publications

Human Emotion Recognition: Review of Sensors and Methods

Human Emotion Recognition: Review of Sensors and Methods

Wearable Cardiorespiratory Monitoring Employing a Multimodal Digital Patch Stethoscope: Estimation of ECG, PEP, LVET and Respiration Using a 55 mm Single-Lead ECG and Phonocardiogram

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

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