Improvement of Dynamic Respiration Monitoring Through Sensor Fusion of Accelerometer and Gyro-sensor

Yoon, Ja-Woong; Noh, Yign; Kwon, Yi-Suk; Kim, Won-Ki; Yoon, Hye‐Young

doi:10.5370/jeet.2014.9.1.334

Cited by 67 publications

(63 citation statements)

References 19 publications

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“…The overall error in respiration rate calculation is obtained 0.53% considering SPR-BTA spirometer as the reference. Thus, we could obtain better accuracy for respiration rate compared to [12], dual straingauge respirometers [31], and [14] in lying position using a single accelerometer. The details of the experiments are brought in Table 2.…”

Section: Experimental Results and Data Analysismentioning

confidence: 99%

“…Figure 3a, depicts a part of respiration signals from sensor and spirometer for the normal breathing pattern of a 30 years old man. As can be seen, even though two signals seem similar, there is an unwanted cumulative error [14] over time that affected their synchronization. We found out that this type of error on signals occurs when two signals have different sampling frequency.…”

Section: Respiration Rate Analysismentioning

confidence: 99%

“…The data acquisition is done with a compact system and data was stored in a laptop. Recently, [14] proposed a fusion method for accelerometer and gyroscope signals to calculate the respiration rate. They considered two types of exercises and the respiration rate errors are calculated as 4.6% and 9.54% for the treadmill and leg press, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the physicians can track the patients wherever they are with devices such as smartphones, tablets or the web regardless of their proximity to the patients. Moreover, in this study the accuracy of respiration rate compared to previous works [14], [12] has been improved in view of rest conditions as well as providing a classification for different breath problems. The overall proposed system is represented in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Medical Cloud-Based Platform for Respiration Rate Measurement and Hierarchical Classification of Breath Disorders

Fekr

Janidarmian

Radecka

et al. 2014

Sensors

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The measurement of human respiratory signals is crucial in cyberbiological systems. A disordered breathing pattern can be the first symptom of different physiological, mechanical, or psychological dysfunctions. Therefore, a real-time monitoring of the respiration patterns, as well as respiration rate is a critical need in medical applications. There are several methods for respiration rate measurement. However, despite their accuracy, these methods are expensive and could not be integrated in a body sensor network. In this work, we present a real-time cloud-based platform for both monitoring the respiration rate and breath pattern classification, remotely. The proposed system is designed particularly for patients with breathing problems (e.g., respiratory complications after surgery) or sleep disorders. Our system includes calibrated accelerometer sensor, Bluetooth Low Energy (BLE) and cloud-computing model. We also suggest a procedure to improve the accuracy of respiration rate for patients at rest positions. The overall error in the respiration rate calculation is obtained 0.53% considering SPR-BTA spirometer as the reference. Five types of respiration disorders, Bradapnea, Tachypnea, Cheyn-stokes, Kaussmal, and Biot's breathing are classified based on hierarchical Support Vector Machine (SVM) with seven different features. We have evaluated the performance of the proposed classification while it is individualized to every subject (case 1) as well as considering all subjects (case 2). Since the selection of kernel function is a key factor to decide SVM's performance, in this paper three different kernel functions are evaluated. The experiments are conducted with 11 subjects and the average accuracy of 94.52% for case 1 and the accuracy of 81.29% for case 2 are achieved based on Radial Basis Function (RBF). Finally, a performance evaluation has been done for normal and impaired subjects considering sensitivity, specificity and G-mean parameters of different kernel functions.

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Section: Experimental Results and Data Analysismentioning

confidence: 99%

Section: Respiration Rate Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Medical Cloud-Based Platform for Respiration Rate Measurement and Hierarchical Classification of Breath Disorders

Fekr

Janidarmian

Radecka

et al. 2014

Sensors

View full text Add to dashboard Cite

show abstract

“…This method is based on on-board signal processing and alarm means. The authors in [37] introduced a fusion algorithm for accelerometer and gyroscope signals to calculate respiration rate. They considered two types of exercises, and the respiration rate errors are calculated as 4.6% and 9.54% versus the piezoelectric respiratory belt for the treadmill and leg press, respectively.…”

Section: Related Workmentioning

confidence: 99%

Design and Evaluation of an Intelligent Remote Tidal Volume Variability Monitoring System in E-Health Applications

Fekr

Radecka

Žilić

2015

IEEE J. Biomed. Health Inform.

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A reliable long-term monitoring and diagnosis of breath disorders at an early stage provides an improvement of medical act, life expectancy, and quality of life while decreasing the costs of treatment and medical services. Therefore, a real-time unobtrusive monitoring of respiration patterns, as well as breath parameters, is a critical need in medical applications. In this paper, we propose an intelligent system for patient home care, capable of measuring respiration rate and tidal volume variability via a wearable sensing technology. The proposed system is designed particularly for the goal of diagnosis and treatment in patients with pathological breathing, e.g., respiratory complications after surgery or sleep disorders. The complete system was comprised of wearable calibrated accelerometer sensor, Bluetooth low energy, and cloud database. The experiments are conducted with eight subjects and the overall error in respiration rate calculation is obtained 0.29%±0.33% considering SPR-BTA spirometer as the reference. We also introduce a method for tidal volume variability estimation while validated using Pearson correlation. Furthermore, since it is essential to detect the critical events resulted from sudden rise or fall in per breath tidal volume of the patients, we provide a technique to automatically find the accurate threshold values based on each individual breath characteristics. Therefore, the system is able to detect the major changes, precisely by more than 98%, and provide immediate feedback such as sound alarm for round-the-clock respiration monitoring.

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Wireless and Multi‐Person Respiration Monitoring using Facemask‐Integrated Flexible Meta‐Antennas

Zheng

et al. 2023

Adv Materials Technologies

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Respiration monitoring of a large population is important in containing the spread of viral respiratory infections such as the coronavirus disease 2019 (COVID‐19). Current technologies, however, lack the ability in respiration monitoring of multiple human subjects in a long‐term, robust, and low‐cost manner. Herein, wireless respiration monitoring of multiple human subjects using facemask‐integrated flexible meta‐antennas is demonstrated. The flexible meta‐antenna has an architecture of multi‐layered anisotropic hole‐array, which is optimized by theory and simulations to achieve high performances including good antenna gain, robustness against body interferences, and high air permeability favorable for facemask integration. A person's respiration patterns and respiration rates are wirelessly obtained by the meta‐antenna integrated with a temperature‐sensor‐embedded chip. Respiration monitoring of multiple subjects in long range and long term during daily activities is simultaneously demonstrated. In addition, a real‐time data processing system is introduced in which a local server, a cloud server, and an application layer are implemented for the real‐time display of respiration patterns and automatic recognition of abnormal status. The design of flexible meta‐antennas may lead to a distinct class of physiological sensors over a large population for applications in pandemic control and personalized healthcare.

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Improvement of Dynamic Respiration Monitoring Through Sensor Fusion of Accelerometer and Gyro-sensor

Cited by 67 publications

References 19 publications

A Medical Cloud-Based Platform for Respiration Rate Measurement and Hierarchical Classification of Breath Disorders

A Medical Cloud-Based Platform for Respiration Rate Measurement and Hierarchical Classification of Breath Disorders

Design and Evaluation of an Intelligent Remote Tidal Volume Variability Monitoring System in E-Health Applications

Wireless and Multi‐Person Respiration Monitoring using Facemask‐Integrated Flexible Meta‐Antennas

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