Estimation of respiratory waveform using an accelerometer

Hung, Phan Duy; Bonnet, Stéphane; Guillemaud, R.; Castelli, Emanuele; Yen, Pai-Chien

doi:10.1109/isbi.2008.4541291

Cited by 67 publications

(55 citation statements)

References 3 publications

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“…Accelerometers worn on the torso are capable of measuring inclination and angular changes during breathing, and then respiratory rate can be estimated using digital signal processing technology. Hung et al 12 proposed a new approach based on a chest biaxial accelerometer to derive respiratory rate during static body conditions (e.g., sitting, standing and lying down). 13 Anmin et al 14 suggested a hybrid principal component analysis (PCA) method (using full angles and the PCA) to derive respiratory rate with a three-dimensional (3D) accelerometer during static body conditions.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Guo

Zhu

et al. 2011

Telemedicine and e-Health

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Respiratory monitoring is widely used in clinical and healthcare practice to detect abnormal cardiopulmonary function during ordinary and routine activities. There are several approaches to estimate respiratory rate, including accelerometer(s) worn on the torso that are capable of sensing the inclination changes due to breathing. In this article, we present an adaptive band-pass filtering method combined with principal component analysis to derive the respiratory rate from threedimensional acceleration data, using a body sensor network platform previously developed by us. In situ experiments with 12 subjects indicated that our method was capable of offering dynamic respiration rate estimation during various body activities such as sitting, walking, running, and sleeping. The experimental studies also suggested that our frequency spectrum-based method was more robust, resilient to motion artifact, and therefore outperformed those algorithms primarily based on spatial acceleration information.

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Section: Introductionmentioning

confidence: 99%

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

Liu

Guo

Zhu

et al. 2011

Telemedicine and e-Health

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show abstract

“…According to the Hung et al's previous study, they used an adaptive filter to remove signal noise of an accelerometer's output. This noise can cause some error calculation of the respiration rate [20]. In this study, our proposed algorithm used sensor fusion through the Kalman filter and applied the Savitzky-Golay smoothing filter; it needed relatively little post-signal processing, so we could calculate the respiration rate efficiently.…”

Section: Signal Processingmentioning

confidence: 99%

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

Yoon¹,

Noh²,

Kwon³

et al. 2014

Journal of Electrical Engineering and Technology

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-In this paper, we suggest a method to improve the fusion of an accelerometer and gyro sensor by using a Kalman filter to produce a more high-quality respiration signal to supplement the weakness of using a single accelerometer. To evaluate our proposed algorithm's performance, we developed a chest belt-type module. We performed experiments consisting of aerobic exercise and muscular exercises with 10 subjects. We compared the derived respiration signal from the accelerometer with that from our algorithm using the standard respiration signal from the piezoelectric sensor in the time and frequency domains during the aerobic and muscular exercises. We also analyzed the time delay to verify the synchronization between the output and standard signals. We confirmed that our algorithm improved the respiratory rate's detection accuracy by 4.6% and 9.54% for the treadmill and leg press, respectively, which are dynamic. We also confirmed a small time delay of about 0.638 s on average. We determined that real-time monitoring of the respiration signal is possible.In conclusion, our suggested algorithm can acquire a more high-quality respiration signal in a dynamic exercise environment away from a limited static environment to provide safer and more effective exercises and improve exercise sustainability.

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“…A technique based upon accelerometers to estimate the breathing waveform is refined from [17] for increased insensitivity to motion and posture changes. Dual-axis accelerometers are placed in the transversal body plane.…”

Section: Breathing Rhythm Monitoringmentioning

confidence: 99%