Single-Frequency Ultrasound-Based Respiration Rate Estimation with Smartphones

Ge, Linfei; Zhang, Jin; Wei, Jing

doi:10.1155/2018/3675974

Cited by 12 publications

(5 citation statements)

References 18 publications

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“…Respiratory rate can also be determined by monitoring the movement of the thorax following emission of ultrasonic waves by a microphone built into a smartphone [8,15]. Because these methods involve ultrasonic irradiation, the smartphone must be placed in front of the chest, making it unsuitable for patients on a bed covered with a thick blanket.…”

Section: Respiratory Rate Monitoring By Smartphonementioning

confidence: 99%

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Identification of Respiratory Sounds Collected from Microphones Embedded in Mobile Phones

Fukuyama

Sugiyama

Chin

et al. 2022

ABE

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Sudden deterioration of condition in patients with various diseases, such as cardiopulmonary arrest, may result in poor outcome even after resuscitation. Early detection of deterioration is important in medical and long-term care settings, regardless of the acute or chronic phase of disease. Early detection and appropriate interventions are essential before resuscitating measures are required. Among the vital signs that indicate the general condition of a patient, respiratory rate has a greater ability to predict serious events such as thromboembolism and sepsis than heart rate and blood pressure, even in early stages. Despite its importance, however, respiratory rate is frequently overlooked and not measured, making it a neglected vital sign. To facilitate the measurement of respiratory rate, a non-invasive method of detecting respiratory sounds was developed based on deep learning technology, using a built-in microphone in a smartphone. Smartphones attached to the bed headboards of 20 participants undergoing polysomnography (PSG) at Kyoto University Hospital recorded respiratory sounds. Sound data were synchronized with overnight respiratory information. After excluding periods of abnormal breathing on the PSG report, sound data were processed for each 1-minute period. Expiration sound was determined using the pressure ow sensor signal on PSG. Finally, a model to identify the expiration section from the sound information was created using a deep learning algorithm from the convolutional Long Short Term Memory network. The accuracy of the learning model in identifying the expiratory section was 0.791, indicating that respiratory rate can be determined using the microphone in a smartphone. By collecting data from more patients and improving the accuracy of this method, respiratory rates could be more easily monitored in all situations, both inside and outside the hospital.

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Section: Respiratory Rate Monitoring By Smartphonementioning

confidence: 99%

“…Methods of monitoring respiratory rate with smartphones include placing smartphones on the face and placing smartphones that emit ultrasonic waves in front of the patient to detect movement of the thorax. These methods are not suitable for continuous monitoring of patientsʼ respiratory rates in hospitals and long-term care settings [8,9].…”

Section: Introductionmentioning

confidence: 99%

Identification of Respiratory Sounds Collected from Microphones Embedded in Mobile Phones

Fukuyama

Sugiyama

Chin

et al. 2022

ABE

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“…Monitoring patient movement patterns via use of accelerometers has the potential to detect the impact of medicines on sleep, daytime sedentary behaviour and daytime drowsiness, of which the latter can be a precursor to falls and motor vehicle or machinery accidents. Vital signs, including heart rate and respiratory rate, both of which can be affected by medicines, could also be monitored by digital technologies . Underwear that can detect incontinence events is being trialled, which may help identify medicines associated with incontinence .…”

Section: Beyond Apps: Monitoring Adverse Effectsmentioning

confidence: 99%

Consumer‐directed technologies to improve medication management and safety

Andrade

Roughead

2019

Medical Journal of Australia

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M edication-related problems are a significant and often undetected health care issue outside of formal medication review services. These problems can delay or prevent clinical improvements and may result in preventable harm. Electronic medication management systems within hospitals are largely assumed to reduce such harm; however, as described by Westbrook and Baysari elsewhere in this Supplement, 1 the evidence for this is limited. Digital technologies for consumers extend the scope of care beyond health organisations and may offer an additional avenue for reducing medication-related problems. While there are several non-digital strategies that help people take their medicines correctly (eg, dose administration aids, home medicines reviews and pharmacy services), digital technologies such as medication management apps and wearable sensor devices can potentially also support medication management by consumers. However, despite this potential, and the launch of smartphones more than a decade ago, consumerdirected digital health tools are seldom used in clinical practice. Medication management: there are (too many) apps for thatOne of the barriers to adoption of digital technologies for medication management is navigating the overcrowded market. A review conducted in 2017 identified more than 800 medication management apps designed to support medication adherence, available from the Windows, iTunes, Google Play and Blackberry app stores. 2 Current medication management apps utilise different strategies to improve medication use, and can be roughly segregated into three categories: education techniques, reminder and management techniques, and behavioural techniques. 3 Education techniques include interactive content, such as structured texts and videos. Reminder and management techniques include options such as alarms, push notifications and short message service (SMS) alerts. Behavioural techniques include personal tracking, where users monitor their own use; external tracking, where data are shared with a health professional, family member or friend to create social accountability and promote the desired behaviour; and gamification, where rewards such as badges or points, are provided for high level adherence.A review of 420 free medication adherence apps conducted in 2018 found the majority (59.5%) employed one mechanism to promote medication adherence, 35.5% used two mechanisms, while only 5.2% used all three mechanisms. 3 Use of reminders was the most popular feature, with 92.1% of all medication adherence apps having some kind of reminder functionality. 3 With regard to behavioural techniques, the authors found that personal tracking was the technique most commonly included, with external tracking and gamification relatively uncommon (5.2% and 1.2%, respectively). 3 It is unclear whether these findings are applicable to paid apps, as they were excluded from this analysis.Studies suggest that app quality varies greatly. The above evaluation of over 800 free and paid apps resulted in a median quality score (bas...

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“…To measure the chest wall movements and extract respiration parameters, several technologies have been used, including marker-based systems [9], laser vibrometry [11], radiofrequency sensors [12,13], terahertz sensors [14], ultrasound [15], RGB cameras [16][17][18] and depth sensors [2]. While marker-based systems are classified as contactless, they still require markers to be attached to the body.…”

Section: Introductionmentioning

confidence: 99%

Human Respiration Rate Measurement with High-Speed Digital Fringe Projection Technique

Lorenz,

Zhang

2023

Sensors

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This paper proposes a non-contact continuous respiration monitoring method based on Fringe Projection Profilometry (FPP). This method aims to overcome the limitations of traditional intrusive techniques by providing continuous monitoring without interfering with normal breathing. The FPP sensor captures three-dimensional (3D) respiratory motion from the chest wall and abdomen, and the analysis algorithms extract respiratory parameters. The system achieved a high Signal-to-Noise Ratio (SNR) of 37 dB with an ideal sinusoidal respiration signal. Experimental results demonstrated that a mean correlation of 0.95 and a mean Root-Mean-Square Error (RMSE) of 0.11 breaths per minute (bpm) were achieved when comparing to a reference signal obtained from a spirometer.

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Single-Frequency Ultrasound-Based Respiration Rate Estimation with Smartphones

Cited by 12 publications

References 18 publications

Identification of Respiratory Sounds Collected from Microphones Embedded in Mobile Phones

Identification of Respiratory Sounds Collected from Microphones Embedded in Mobile Phones

Consumer‐directed technologies to improve medication management and safety

Human Respiration Rate Measurement with High-Speed Digital Fringe Projection Technique

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