Breathing Detection: Towards a Miniaturized, Wearable, Battery-Operated Monitoring System

Corbishley, P.; Rodriguez‐Villegas, Esther

doi:10.1109/tbme.2007.910679

Cited by 192 publications

(114 citation statements)

References 16 publications

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“…4 is the flowchart of the overall signal processing. The general respiration frequency range is distributed between 0 and 1 Hz [19]. Thus, we sampled the output data from the accelerometer and gyro sensor at 20 Hz.…”

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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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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“…A respiration rate measurement system using this principle was reported by Werthammer et al (1983).The sounds generated by snorting, speaking, crying, coughing negatively affected the operation of the system. A microphone placed on the neck enabled a respiration signal to be produced and provided a miniaturised wearable 2 respiration monitoring system (Corbishley and Rodriguez-Villegas, 2008).…”

Section: Contact Respiration Rate Monitoringmentioning

confidence: 99%

An Evaluation of Thermal Imaging Based Respiration Rate Monitoring in Children

Al-Khalidi

Saatchi

Elphick

et al. 2011

American Journal of Engineering and Applied Sciences

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Problem statement: An important indicator of an individual's health is respiration rate. It is the average number of times air is inhaled and exhaled per minute. Existing respiration monitoring methods require an instrument to be attached to the patient's body during the recording. This is a discomfort to the patient and the instrument can be dislodged from its position. Approach: In this study a novel noncontact, thermal imaging based respiration rate measurement method is developed and evaluated. Facial thermal videos of 16 children (age: Median = 6.5 years, minimum = 6 months, maximum = 17 years) were processed in the study. The recordings were carried out while the children rested comfortably on a bed. The children's respiration rates were also simultaneously measured using a number of conventional contact based methods. Results: This allowed comparisons with the thermal imaging method to be carried out. The image capture rate was 50 frames per second and the duration of a thermal video recording was 2 min per child. The thermal images were filtered and segmented to identify the nasal region. An algorithm was developed to automatically track the identified nasal area. This region was partitioned into eight equal concentric segments. The pixel values within each segment were averaged to produce a single thermal feature for that segment of the image. A respiration signal was obtained by plotting each segment's feature against time. Conclusion: Respiration rate values were automatically calculated by determining the number of oscillations in the respiration signals per minute. A close correlation (coefficient = 0.994) was observed between the respiration rates measured using the thermal imaging method and those obtained using the most effective conventional contact based respiration method.

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“…[7][8][9][10] However, there are often several unique challenges to designing and implementing a system capable of monitoring physical activity without the use of large equipment. 10 As Corbishley and Rodriguez-Villegas 11 acknowledge, an ideal system must use small sensors, such that they do not interfere with the individual's movement pattern. The size of the device has important implications for its function, including how big the battery must be, the weight of the sensors selected, and the system's processing power.…”

Section: Introductionmentioning

confidence: 99%

Accuracy and Precision of an Accelerometer-Based Smartphone App Designed to Monitor and Record Angular Movement over Time

Bittel

Elazzazi

Bittel

2016

Telemedicine and e-Health

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"Accuracy and precision of an accelerometer-based smartphone app designed to monitor and record angular movement over time. " Telemedicine and e-Health.22,4. 302-309. (2016 AbstractBackground: Therapeutic exercise is a central component in the management of many common conditions. It is imperative, therefore, that clinicians monitor and correct patient performance to facilitate the use of proper form both in the clinic and during home exercise programs. Although clinicians are trained to prescribe exercise and analyze form, there are many subtleties that may be missed by relying on visual assessment. This study investigated the accuracy and precision of a novel, exercise-training smartphone application (app), running on an iPhone Ò (Apple, Cupertino, CA) 4

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Breathing Detection: Towards a Miniaturized, Wearable, Battery-Operated Monitoring System

Cited by 192 publications

References 16 publications

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

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

An Evaluation of Thermal Imaging Based Respiration Rate Monitoring in Children

Accuracy and Precision of an Accelerometer-Based Smartphone App Designed to Monitor and Record Angular Movement over Time

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