A Real-Time Vision-Based Heart Rate Measurement Framework for Home Nursing Assistance

Wu, Han; Wang, Tao; Dai, Tuo; Lin, Yuanzhen; Wang, Yizhou

doi:10.1145/3265689.3265718

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…The aim of the present study was to assess in a controlled experimental setting the accuracy of the Facereader rPPG for remote HR monitoring with respect to a gold standard electrocardiograph. Although, recent evidence suggests that reliable HR measurement can be achieved using different rPPG algorithms [25, 49, 51, 55, 56], the need of further validations and cross comparisons is crucial.…”

Section: Discussionmentioning

confidence: 99%

“…Although on the market there are probably alternative and more advanced technologies such as the Vital Signs Camera by Philips, which is available for licensing to third part manufacturers, to the authors’ knowledge, the rPPG by Noldus is the only consumer product available on the market and up until today just one study involving this specific tool has been carried out [54]. Although recent evidence suggests that reliable HR measurement can be achieved using different rPPG algorithms [25, 49, 51, 55, 56], the need of further validations and cross comparisons is crucial. The objective of this study is to contribute to the improvement of this kind of technology, which has the potential to assess and monitoring the personal psychophysical status in a simple, convenient and non-invasive way with important applications (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Remote heart rate monitoring - Assessment of the Facereader rPPg by Noldus

Benedetto¹,

Caldato²,

Greenwood

et al. 2019

PLoS ONE

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Remote photoplethysmography (rPPG) allows contactless monitoring of human cardiac activity through a video camera. In this study, we assessed the accuracy and precision for heart rate measurements of the only consumer product available on the market, namely the FacereaderTM rPPG by Noldus, with respect to a gold standard electrocardiograph. Twenty-four healthy participants were asked to sit in front of a computer screen and alternate two periods of rest with two stress tests (i.e. Go/No-Go task), while their heart rate was simultaneously acquired for 20 minutes using the ECG criterion measure and the FacereaderTM rPPG. Results show that the FacereaderTM rPPG tends to overestimate lower heart rates and underestimate higher heart rates compared to the ECG. The Facereader™ rPPG revealed a mean bias of 9.8 bpm, the 95% limits of agreement (LoA) ranged from almost -30 up to +50 bpm. These results suggest that whilst the rPPG FacereaderTM technology has potential for contactless heart rate monitoring, its predictions are inaccurate for higher heart rates, with unacceptable precision across the entire range, rendering its estimates unreliable for monitoring individuals.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remote heart rate monitoring - Assessment of the Facereader rPPg by Noldus

Benedetto¹,

Caldato²,

Greenwood

et al. 2019

PLoS ONE

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“…Compared to the conference paper of our precedent work published in ICAA2018 (Wu et al, 2018), this paper put more effort in the multiple ROIs fusion part which will be explained in Section 3. We also supplemented our experiment to validate this method.…”

Section: Related Workmentioning

confidence: 99%

Let home nursing assistant robots see your heart rate

Wang

Dai

et al. 2018

IJCS

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Purpose -This paper aims to design a vision-based non-contact real-time accurate heart rate (HR) measurement framework for home nursing assistant.Design/methodology/approach -The study applied Second-Order Blind Signal Identification (SOBI) algorithm to extract remote HR signal and analyzed it with Fast Fourier Transform (FFT). Multiple regions of interest are chosen and analyzed to obtain a more accurate result.Findings -An accurate non-contact hear rate (HR) measurement framework is proposed and proved to be efficient.Originality/value -The contributions of this HR measurement framework are as follows: accurate measurement of HR, real-time performance, robust under various scenes such as conversation, lightweight computation which is suitable and necessary for home nursing assistance. This framework is designed to be flexibly used in various real-life scenes such as domestic health assistance and affectively intelligent agents and is proved to be robust under such scenes.

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Continuous Monitoring of Vital Signs Using Cameras: A Systematic Review

Selvaraju

Spicher

Wang

et al. 2022

Sensors

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In recent years, noncontact measurements of vital signs using cameras received a great amount of interest. However, some questions are unanswered: (i) Which vital sign is monitored using what type of camera? (ii) What is the performance and which factors affect it? (iii) Which health issues are addressed by camera-based techniques? Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement, we conduct a systematic review of continuous camera-based vital sign monitoring using Scopus, PubMed, and the Association for Computing Machinery (ACM) databases. We consider articles that were published between January 2018 and April 2021 in the English language. We include five vital signs: heart rate (HR), respiratory rate (RR), blood pressure (BP), body skin temperature (BST), and oxygen saturation (SpO2). In total, we retrieve 905 articles and screened them regarding title, abstract, and full text. One hundred and four articles remained: 60, 20, 6, 2, and 1 of the articles focus on HR, RR, BP, BST, and SpO2, respectively, and 15 on multiple vital signs. HR and RR can be measured using red, green, and blue (RGB) and near-infrared (NIR) as well as far-infrared (FIR) cameras. So far, BP and SpO2 are monitored with RGB cameras only, whereas BST is derived from FIR cameras only. Under ideal conditions, the root mean squared error is around 2.60 bpm, 2.22 cpm, 6.91 mm Hg, 4.88 mm Hg, and 0.86 °C for HR, RR, systolic BP, diastolic BP, and BST, respectively. The estimated error for SpO2 is less than 1%, but it increases with movements of the subject and the camera-subject distance. Camera-based remote monitoring mainly explores intensive care, post-anaesthesia care, and sleep monitoring, but also explores special diseases such as heart failure. The monitored targets are newborn and pediatric patients, geriatric patients, athletes (e.g., exercising, cycling), and vehicle drivers. Camera-based techniques monitor HR, RR, and BST in static conditions within acceptable ranges for certain applications. The research gaps are large and heterogeneous populations, real-time scenarios, moving subjects, and accuracy of BP and SpO2 monitoring.

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A Real-Time Vision-Based Heart Rate Measurement Framework for Home Nursing Assistance

Cited by 3 publications

References 10 publications

Remote heart rate monitoring - Assessment of the Facereader rPPg by Noldus

Remote heart rate monitoring - Assessment of the Facereader rPPg by Noldus

Let home nursing assistant robots see your heart rate

Continuous Monitoring of Vital Signs Using Cameras: A Systematic Review

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