Noncontact pulse wave detection by two-band infrared video-based measurement on face without visible lighting

Mitsuhashi, Ryota; Okada, Genki; Kurita, Koki; Kagawa, Keiichiro; Kawahito, Shoji; Koopipat, Chawan; Tsumura, Norimichi

doi:10.1007/s10015-018-0430-5

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…In the current study, two-band images were used: one with a central wavelength of 800 nm and the other with a central wavelength of 930 nm. These two wavelengths were selected based on a previous study [10]. By using the temporal variation in the acquired average pixel values in Equations ( 4) and ( 5), respectively, the estimation results of the original pulse wave signal by the conventional and proposed methods could be obtained.…”

Section: Acquisition Of the Original Pulse Wave Signal And Signal Pro...mentioning

confidence: 99%

“…The AER between the estimated heart rate and the heart rate obtained using a pulse wave meter was determined [10]. AER is expressed by the following equation:…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…However, a single-band camera has the problem that the pulse wave estimation is affected by illumination in an environment with illumination fluctuation. Mitsuhashi et al proposed a pulse wave estimation method that can be used in dark areas by using two-band near-infrared video images [10]. This method used two-band near-infrared video images to separate hemoglobin and shade components.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, therefore, we propose a method for estimating pulse waves in dark areas while considering the effect of noise. Our proposed method is a modified version of Mitsuhashi et al's method [10]. The Wiener estimation method was used for pulse wave estimation to consider noise.…”

Section: Introductionmentioning

confidence: 99%

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Noise-Robust Pulse Wave Estimation from Near-Infrared Face Video Images Using the Wiener Estimation Method

Hino,

Ashida,

Ogawa-Ochiai

et al. 2023

J. Imaging

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In this paper, we propose a noise-robust pulse wave estimation method from near-infrared face video images. Pulse wave estimation in a near-infrared environment is expected to be applied to non-contact monitoring in dark areas. The conventional method cannot consider noise when performing estimation. As a result, the accuracy of pulse wave estimation in noisy environments is not very high. This may adversely affect the accuracy of heart rate data and other data obtained from pulse wave signals. Therefore, the objective of this study is to perform pulse wave estimation robust to noise. The Wiener estimation method, which is a simple linear computation that can consider noise, was used in this study. Experimental results showed that the combination of the proposed method and signal processing (detrending and bandpass filtering) increased the SNR (signal to noise ratio) by more than 2.5 dB compared to the conventional method and signal processing. The correlation coefficient between the pulse wave signal measured using a pulse wave meter and the estimated pulse wave signal was 0.30 larger on average for the proposed method. Furthermore, the AER (absolute error rate) between the heart rate measured with the pulse wave meter was 0.82% on average for the proposed method, which was lower than the value of the conventional method (12.53% on average). These results show that the proposed method is more robust to noise than the conventional method for pulse wave estimation.

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Section: Acquisition Of the Original Pulse Wave Signal And Signal Pro...mentioning

confidence: 99%

“…The AER between the estimated heart rate and the heart rate obtained using a pulse wave meter was determined [10]. AER is expressed by the following equation:…”

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Noise-Robust Pulse Wave Estimation from Near-Infrared Face Video Images Using the Wiener Estimation Method

Hino,

Ashida,

Ogawa-Ochiai

et al. 2023

J. Imaging

Self Cite

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“…The only hardware required to perform rPPG imaging is a standard camera. Although several techniques based on the use of infra-red (IR) or near infra-red (NIR) cameras exist [43, 44], the most developed and employed algorithms use a colour model method based on red, green and blue (RGB) imaging to acquire a signal from a distance of up to several meters [45, 46, 47]. In technical terms, the main difference between these two methods lies in the fact that both IR and NIR cameras allow a more accurate estimate of HR parameters and exploit the information provided by blood volume variation of vessels.…”

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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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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Stress levels estimation from facial video based on non-contact measurement of pulse wave

Iuchi

Mitsuhashi

Goto

et al. 2020

Artif Life Robotics

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Noncontact pulse wave detection by two-band infrared video-based measurement on face without visible lighting

Cited by 5 publications

References 9 publications

Noise-Robust Pulse Wave Estimation from Near-Infrared Face Video Images Using the Wiener Estimation Method

Noise-Robust Pulse Wave Estimation from Near-Infrared Face Video Images Using the Wiener Estimation Method

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

Stress levels estimation from facial video based on non-contact measurement of pulse wave

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