“Real-time monitoring of heart rate by processing of Microsoft Kinect™ 2.0 generated streams”

Bosi, Ilaria; Cogerino, Chiara; Bazzani, Marco

doi:10.1109/splitech.2016.7555944

Cited by 15 publications

(5 citation statements)

References 20 publications

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“…Gupta et al [13] reported a mean error of 1.80 for their proposed system, while Li et al [21] achieved a mean error of -3.30. Furthermore, Bosi et al [16] reported a MAE equal to 10.054. It is evident that the proposed approach performed better than the examined approaches.…”

Section: Resultsmentioning

confidence: 98%

“…Similarly, a Kinect TM camera has been used in [14] for measurement of heart rate and respiration rate, while in [15], Mishra et al presented a blood pulse measurement system using linearly polarized light. The Kinect TM v2.0 camera has also been used in [16] for remote detection of heart rate. After filtering the RGB data, blind source separation (BSS) is performed using PCA.…”

Section: Introductionmentioning

confidence: 99%

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Remote Heart Rate Extraction Using Microsoft KinectTM v2.0

Malasinghe

Katsigiannis

Ramzan

et al. 2018

Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology

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Remote and contactless heart rate detection is still an open research issue of great clinical importance. Available approaches lack the necessary accuracy and reliability for acceptance by medical experts. In this study, we propose a new method for remote heart rate extraction using the Microsoft Kinect TM v2.0 image sensor. The proposed approach relies on signal processing and machine learning methods in order to create a model for accurate estimation of the heart rate via RGB and infrared face videos. Electrocardiography (ECG) recordings and RGB and infrared face videos, captured using the Kinect TM v2.0 image sensor, were acquired from 17 subjects and used to create a machine learning model for remote heart rate detection. Experimental evaluation through supervised regression experiments showed that the proposed approach achieved a mean absolute error of 6.972 bpm, demonstrating the capabilities of the underlying technology.

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Section: Resultsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Remote Heart Rate Extraction Using Microsoft KinectTM v2.0

Malasinghe

Katsigiannis

Ramzan

et al. 2018

Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology

View full text Add to dashboard Cite

show abstract

“…ECG is performed by placing electrodes on the human body and analyzing electrical impulses to determine heart rate [8,11,13,14,15]. PPG is the measurement of blood volume change in tissue in relation to heartbeat using an optical sensing environment that includes a light source and detector [8,12,16,17,18,19,20].…”

Section: ░ 2 Related Workmentioning

confidence: 99%

IoT Based Pulse Oximeter for Remote Health Assessment: Design, Challenges and Futuristic Scope

Revathi¹,

Tamilselvi²,

Gomathi³

et al. 2022

IJEER

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The Internet of Things (IoT) comprises the networking, computing, and storage with analytics technologies that do wonders in every aspect of human life through its applications and turns their life style as smart as possible. The application of IoT in healthcare domain would transform the medical service to be timely accessible and affordable by all people. The cardiovascular diseases (CVD) are marked as one of the most common cause of death around the world. A research study states that CVD targets the public with age limit of 30 - 60 belongs to developing countries like India in an evidential growth. The continuous monitoring of human heart, which is a fist sized strongest muscle through invasive sensors helps in early detection and anticipating necessary treatment on time. This induces a design of IoT enabled pulse rate monitoring system to continuously track the patient at anywhere and better serve them at any time through any device. The device uses easy pulse sensor and is operated through Raspberry Pi. The effectiveness of the device is analyzed against one of the top brand fingertip pulse oximeter, suggested for home and clinical usage in practice. Further the non-invasive design of pulse oximeter which determines the blood pressure (BP) using electrocardiography (ECG) and photo plethysmography (PPG) sensors.

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“…In [6], a charge coupled device (CCD) camera for the acquisition of the person's face is used to extract the heart rate and the respiratory rate. In [13], a non-invasive real-time heart rate monitoring system is proposed based on the Microsoft device Kinect TM version 2.0. In [14], a real-time monitoring system is proposed where a webcam is used to measure the heart rate of car drivers.…”

Section: Introductionmentioning

confidence: 99%

Contact-Less Real-Time Monitoring of Cardiovascular Risk Using Video Imaging and Fuzzy Inference Rules

et al. 2018

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Conventional methods for measuring cardiovascular parameters use skin contact techniques requiring a measuring device to be worn by the user. To avoid discomfort of contact devices, camera-based techniques using photoplethysmography have been recently introduced. Nevertheless, these solutions are typically expensive and difficult to be used daily at home. In this work, we propose an innovative solution for monitoring cardiovascular parameters that is low cost and can be easily integrated within any common home environment. The proposed system is a contact-less device composed of a see-through mirror equipped with a camera that detects the person’s face and processes video frames using photoplethysmography in order to estimate the heart rate, the breath rate and the blood oxygen saturation. In addition, the color of lips is automatically detected via clustering-based color quantization. The estimated parameters are used to predict a risk of cardiovascular disease by means of fuzzy inference rules integrated in the mirror-based monitoring system. Comparing our system to a contact device in measuring vital parameters on still or slightly moving subjects, we achieve measurement errors that are within acceptable margins according to the literature. Moreover, in most cases, the response of the fuzzy rule-based system is comparable with that of the clinician in assessing a risk level of cardiovascular disease.

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“Real-time monitoring of heart rate by processing of Microsoft Kinect™ 2.0 generated streams”

Cited by 15 publications

References 20 publications

Remote Heart Rate Extraction Using Microsoft KinectTM v2.0

Remote Heart Rate Extraction Using Microsoft KinectTM v2.0

IoT Based Pulse Oximeter for Remote Health Assessment: Design, Challenges and Futuristic Scope

Contact-Less Real-Time Monitoring of Cardiovascular Risk Using Video Imaging and Fuzzy Inference Rules

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