An evaluation of a video magnification-based system for respiratory rate monitoring in an acute mental health setting

Laurie, Jordan; Higgins, Niall; Peynot, Thierry; Fawcett, Lisa; Roberts, J.O.

doi:10.1016/j.ijmedinf.2021.104378

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…The model achieved 85% accuracy in extracting the blood pressure. Laurie et al further demonstrated how an algorithm specifically designed to control exposure time during video capture improves the accuracy of rPPG (Laruie et al, 2021).…”

Section: Remote Patient Monitoring Architecturesmentioning

confidence: 99%

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

Shaik

Tao

Higgins

et al. 2023

WIREs Data Min & Knowl

101

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The adoption of artificial intelligence (AI) in healthcare is growing rapidly.Remote patient monitoring (RPM) is one of the common healthcare applications that assist doctors to monitor patients with chronic or acute illness at remote locations, elderly people in-home care, and even hospitalized patients. The reliability of manual patient monitoring systems depends on staff time management which is dependent on their workload. Conventional patient monitoring involves invasive approaches which require skin contact to monitor health status. This study aims to do a comprehensive review of RPM systems including adopted advanced technologies, AI impact on RPM, challenges and trends in AI-enabled RPM. This review explores the benefits and challenges of patient-centric RPM architectures enabled with Internet of Things wearable devices and sensors using the cloud, fog, edge, and blockchain technologies. The role of AI in RPM ranges from physical activity classification to chronic disease monitoring and vital signs monitoring in emergency settings. This review results show that AI-enabled RPM architectures have transformed healthcare monitoring applications because of their ability to detect early deterioration in patients' health, personalize individual patient health parameter monitoring using federated learning, and learn human behavior patterns using techniques such as reinforcement learning. This review discusses the challenges and trends to adopt AI to RPM systems and implementation issues. The future directions of AI in RPM applications are analyzed based on the challenges and trends.

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Section: Remote Patient Monitoring Architecturesmentioning

confidence: 99%

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

Shaik

Tao

Higgins

et al. 2023

WIREs Data Min & Knowl

101

View full text Add to dashboard Cite

show abstract

“…In [13], the authors evaluated EVM on RGB video streams using two human subjects and two monkey subjects, measuring the mean pulse rates with an accuracy equal to 93.2% for humans and 97.3% for monkeys based on Equation (1). For respiratory rate estimation via EVM, an error rate of 1.5% was reported, which based on Equation (1) equals 98.5% accuracy [14].…”

Section: Eulerian Video Magnification (Evm)mentioning

confidence: 99%

Contactless Camera-Based Heart Rate and Respiratory Rate Monitoring Using AI on Hardware

Kolosov

Kelefouras

Kourtessis

et al. 2023

Sensors

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Detecting vital signs by using a contactless camera-based approach can provide several advantages over traditional clinical methods, such as lower financial costs, reduced visit times, increased comfort, and enhanced safety for healthcare professionals. Specifically, Eulerian Video Magnification (EVM) or Remote Photoplethysmography (rPPG) methods can be utilised to remotely estimate heart rate and respiratory rate biomarkers. In this paper two contactless camera-based health monitoring architectures are developed using EVM and rPPG, respectively; to this end, two different CNNs, (Mediapipe’s BlazeFace and FaceMesh) are used to extract suitable regions of interest from incoming video frames. These two methods are implemented and deployed on four off-the-shelf edge devices as well as on a PC and evaluated in terms of latency (in each stage of the application’s pipeline), throughput (FPS), power consumption (Watt), efficiency (throughput/Watt), and value (throughput/cost). This work provides important insights about the computational costs and bottlenecks of each method on each hardware platform, as well as which platform to use depending on the target metric. One of our insights shows that the Jetson Xavier NX platform is the best platform in terms of throughput and efficiency, while Raspberry Pi 4 8 GB is the best platform in terms of value.

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“…Still, manual counting of a one-minute sample recording is widely practiced in various evaluation protocols [28][29][30]. For this reason, only a one-minute sample was recorded for each subject for the evaluation of the RR algorithm.…”

Section: Respiratory Rate Estimationmentioning

confidence: 99%

Remote Monitoring of Patient Respiration with Mask Attachment—A Pragmatic Solution for Medical Facilities

Koh¹,

Ang²,

Ser

et al. 2021

Inventions

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Remote monitoring of vital signs in infectious patients minimizes the risks of viral transmissions to healthcare professionals. Donning face masks could reduce the risk of viral transmissions and is currently practiced in medical facilities. An acoustic-sensing device was attached to face masks to assist medical facilities in remotely monitoring patients’ respiration rate and wheeze occurrence. Usability and functionality studies of the modified face mask were evaluated on 16 healthy participants. Participants were blindfolded throughout the data collection process. Respiratory rates of the participants were evaluated for one minute. The wheeze detection algorithm was assessed by playing 176 wheezes and 176 normal breaths through a foam mannequin. No discomfort was reported from the participants who used the modified mask. The mean error of respiratory rate was found to be 2.0 ± 1.3 breath per minute. The overall accuracy of the wheeze detection algorithm was 91.9%. The microphone sensor that was first designed to be chest-worn has been proven versatile to be adopted as a mask attachment. The current findings support and suggest the use of the proposed mask attachment in medical facilities. This application can be especially helpful in managing a sudden influx of patients in the face of a pandemic.

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An evaluation of a video magnification-based system for respiratory rate monitoring in an acute mental health setting

Cited by 8 publications

References 26 publications

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

Contactless Camera-Based Heart Rate and Respiratory Rate Monitoring Using AI on Hardware

Remote Monitoring of Patient Respiration with Mask Attachment—A Pragmatic Solution for Medical Facilities

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