Estimation of Respiration Rate from Three-Dimensional Acceleration Data Based on Body Sensor Network

Liu, Guanzheng; Guo, Yanwei; Zhu, Qingsong; Huang, Baotong; Wang, Lei

doi:10.1089/tmj.2011.0022

Cited by 83 publications

(68 citation statements)

References 23 publications

(24 reference statements)

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“…The sensor technologies include thermal, humidity, acoustic, pressure, resistive, inductive, acceleration, electromyography, and impedance. A wearable device with these sensors can be mounted into chest belts, attached to a chest belt [40][41][42][43], or applied to the skin [44,45], amongst other modes of attachment.…”

Section: B Respiratory Ratementioning

confidence: 99%

“…But monitoring with a face mask can still be intrusive to users, and the displacement of the sensor may affect the accuracy. [47], RespiraSense™ patch (PMD Solutions, Ireland) [48], and MonBaby clip (MonBaby, New York, USA) [49], (b) Zephyr™ garment (Zephyr Technology, Auckland, New Zealand) [50], and state-of-the-art research (c) an epidermal thermal sensor [45], (d) humidity sensor [46], (e) wearable strain gauge [41], (f) waist-wearable triboelectric sensor, (g) breathing belt with 3D accelerometer [42], and (h) a BandAid like respiratory monitor [44].…”

Section: B Respiratory Ratementioning

confidence: 99%

“…Another study by Liu et al reported a body sensor network (BSN) enabled threedimensional accelerator to track inclination changes due to breathing. By extracting breathing information with principal component analysis, dynamic RR estimation can be obtained during various activities such as walking, running and sleeping [42].…”

Section: B Respiratory Ratementioning

confidence: 99%

See 2 more Smart Citations

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Ding

Clifton

et al. 2021

IEEE Rev. Biomed. Eng.

194

155

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Coronavirus disease 2019 (COVID-19) has emerged as a pandemic with serious clinical manifestations including death.A pandemic at the large-scale like COVID-19 places extraordinary demands on the world's health systems, dramatically devastates vulnerable populations, and critically threatens the global communities in an unprecedented way. While tremendous efforts at the frontline are placed on detecting the virus, providing treatments and developing vaccines, it is also critically important to examine the technologies and systems for tackling disease emergence, arresting its spread and especially the strategy for diseases prevention. The objective of this article is to review enabling technologies and systems with various application scenarios for handling the COVID-19 crisis. The article will focus specifically on 1) wearable devices suitable for monitoring the populations at risk and those in quarantine, both for evaluating the health status of caregivers and management personnel, and for facilitating triage processes for admission to hospitals; 2) unobtrusive sensing systems for detecting the disease and for monitoring patients with relatively mild symptoms whose clinical situation could suddenly worsen in improvised hospitals; and 3) telehealth technologies for the remote monitoring and diagnosis of COVID-19 and related diseases. Finally, further challenges and opportunities for future directions of development are highlighted.

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Section: B Respiratory Ratementioning

confidence: 99%

Section: B Respiratory Ratementioning

confidence: 99%

See 1 more Smart Citation

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Ding

Clifton

et al. 2021

IEEE Rev. Biomed. Eng.

194

155

View full text Add to dashboard Cite

show abstract

“…MC301 made by Wacoh was used in ambulatory monitoring to find human posture and walking velocity [93]. MMA8451Q (Austin, TX, USA), a triple axis, low power, capacitive digital accelerometer (freescale semiconductor) [94], a triaxial accelerometer (patch sensor device designed by Vital Connect, Inc. (Campbell, CA)) [95], and inbuilt on-board 3-axis accelerometer SCA3000 [96] were used in extraction of respiratory rate. And also a triaxial accelerometer was used to measure the body movements [90] or daily stress [97] and for left ventricular functions monitoring [98].…”

Section: Designing Of External Cardiac Loop Recordermentioning

confidence: 99%

An Engineering Perspective of External Cardiac Loop Recorder: A Systematic Review

Srinivasulu

Sriraam

2016

Journal of Medical Engineering

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External cardiac loop recorder (ELR) is a kind of ECG monitoring system that records cardiac activities of a subject continuously for a long time. When the heart palpitations are not the frequent and nonspecific character, it is difficult to diagnose the disease. In such a case, ELR is used for long-term monitoring of heart signal of the patient. But the cost of ELR is very high. Therefore, it is not prominently available in developing countries like India. Since the design of ELR includes the ECG electrodes, instrumentation amplifier, analog to digital converter, and signal processing unit, a comparative review of each part of the ELR is presented in this paper in order to design a cost effective, low power, and compact kind of ELR. This review will also give different choices available for selecting and designing each part of the ELR system. Finally, the review will suggest the better choice for designing a cost effective external cardiac loop recorder that helps to make it available even for rural people in India.

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“…us, analysis of such type of signals using traditional methods and visual detection is challenging, inappropriate, and timeconsuming. Moreover, the parameters of HRV are affected by respiration [6], instantaneous variation [7], and motion artifacts [8]. us, to minimize these obstacles of visual and manual interpretation, researchers developed computeraided diagnostic (CAD) techniques for HRV analysis.…”

Section: Introductionmentioning

confidence: 99%

Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques

Hussain

Awan

Aziz

et al. 2020

BioMed Research International

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The adaptability of heart to external and internal stimuli is reflected by the heart rate variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes. Based on the nonlinear, nonstationary, and highly complex dynamics of the controlling mechanism of the cardiovascular system, linear HRV measures have limited capability to accurately analyze the underlying dynamics. In this study, we propose an automated system to analyze HRV signals by extracting multimodal features to capture temporal, spectral, and complex dynamics. Robust machine learning techniques, such as support vector machine (SVM) with its kernel (linear, Gaussian, radial base function, and polynomial), decision tree (DT), k-nearest neighbor (KNN), and ensemble classifiers, were employed to evaluate the detection performance. Performance was evaluated in terms of specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). The highest performance was obtained using SVM linear kernel (TA = 93.1%, AUC = 0.97, 95% CI [lower bound = 0.04, upper bound = 0.89]), followed by ensemble subspace discriminant (TA = 91.4%, AUC = 0.96, 95% CI [lower bound 0.07, upper bound = 0.81]) and SVM medium Gaussian kernel (TA = 90.5%, AUC = 0.95, 95% CI [lower bound = 0.07, upper bound = 0.86]). The results reveal that the proposed approach can provide an effective and computationally efficient tool for automatic detection of congestive heart failure patients.

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Estimation of Respiration Rate from Three-Dimensional Acceleration Data Based on Body Sensor Network

Cited by 83 publications

References 23 publications

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

An Engineering Perspective of External Cardiac Loop Recorder: A Systematic Review

Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques

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