Unsupervised skin tissue segmentation for remote photoplethysmography

Bobbia, Serge; Macwan, Richard; Benezeth, Yannick; Mansouri, Alamin; Dubois, Julien

doi:10.1016/j.patrec.2017.10.017

Cited by 355 publications

(198 citation statements)

References 25 publications

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“…Apart from the selected PURE data set, there are however other data sets that contain uncompressed/lossless compressed videos and could be used instead: the ECG-Fitness database (Špetlík, Franc & Matas, 2018), the MMSE-HR (Tulyakov et al, 2016) and UBFC-RPPG database (Bobbia et al, 2017). Yet the first contains videos of subjects during physical activities, whereas we wanted to initially assess the validity of UST-PRV metrics extracted from videos containing stationary subjects and/or subjects with controlled head motion (to first study the validity of rPPG-derived UST-PRV metrics for subjects in close-to-resting conditions).…”

Section: Discussionmentioning

confidence: 99%

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Feasibility of assessing ultra-short-term pulse rate variability from video recordings

Finžgar

Podržaj

2020

PeerJ

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Objectives: Remote photoplethysmography (rPPG) is a promising non-contact measurement technique for assessing numerous physiological parameters: pulse rate, pulse rate variability (PRV), respiratory rate, pulse wave velocity, blood saturation, blood pressure, etc. To justify its use in ultra-short-term (UST) PRV analysis, which is of great benefit for several healthcare applications, the agreement between rPPGand PPG-derived UST-PRV metrics was studied. Approach: Three time-domain metrics-standard deviation of normal-to-normal (NN) intervals (SDNN), root mean square of successive NN interval differences (RMSSD), and the percentage of adjacent NN intervals that differ from each other by more than 50 ms (pNN50)-were extracted from 56 video recordings in a publicly available data set. The selected metrics were calculated on the basis of three groups of 10 s recordings and their average, two groups of 30 s recordings and their average, and a group of 60 s recordings taken from the full-length recordings and then compared with metrics derived from the corresponding reference (PPG) pulse waveform signals by using correlation and effect size parameters, and Bland-Altman plots.Main results: The results show there is stronger agreement as the recording length increases for SDNN and RMSSD, yet there is no significant change for pNN50. The agreement parameters reach r = 0.841 (p < 0.001), r = 0.529 (p < 0.001), and r = 0.657 (p < 0.001), estimated median bias −1.52, −2.28 ms and −1.95% and a small effect size for SDNN, RMSSD, and pNN50 derived from the 60 s recordings, respectively. Significance: Remote photoplethysmography-derived UST-PRV metrics manage to capture UST-PRV metrics derived from reference (PPG) recordings well. This feature is highly desirable in numerous applications for the assessment of one's health and well-being. In future research, the validity of rPPG-derived UST-PRV metrics compared to the gold standard electrocardiography recordings is to be assessed.

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Section: Discussionmentioning

confidence: 99%

“…The second data set requires the payment of handling fees to be given access. The third one, the recently published UBFC-RPPG database (Bobbia et al, 2017) offers fewer videos (50) than the selected data set. We therefore decided to use PURE.…”

Section: Discussionmentioning

confidence: 99%

Feasibility of assessing ultra-short-term pulse rate variability from video recordings

Finžgar

Podržaj

2020

PeerJ

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“…The video streams are, however, compressed. PURE, a dataset introduced in [ [20], which contains 43 uncompressed videos along with finger oximeter signals. The participants played a time-sensitive mathematical game that supposedly raises their pulse rate.…”

Section: Datasetsmentioning

confidence: 99%

“…Several biomedical parameters can be computed from PPG signals: blood oxygen level, also known as peripheral oxygen saturation (SpO 2 ) [9,10], breathing rate [11][12][13], blood pressure by pulse transit time estimation [14,15], peripheral vasomotor activity [16,17], and vascular occlusion [18]. Imaging PPG have also been used to identify living skin in images [19,20].…”

Section: Introductionmentioning

confidence: 99%

3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video

2019

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Remote pulse rate measurement from facial video has gained particular attention over the last few years. Research exhibits significant advancements and demonstrates that common video cameras correspond to reliable devices that can be employed to measure a large set of biomedical parameters without any contact with the subject. A new framework for measuring and mapping pulse rate from video is presented in this pilot study. The method, which relies on convolutional 3D networks, is fully automatic and does not require any special image preprocessing. In addition, the network ensures concurrent mapping by producing a prediction for each local group of pixels. A particular training procedure that employs only synthetic data is proposed. Preliminary results demonstrate that this convolutional 3D network can effectively extract pulse rate from video without the need for any processing of frames. The trained model was compared with other state-of-the-art methods on public data. Results exhibit significant agreement between estimated and ground-truth measurements: the root mean square error computed from pulse rate values assessed with the convolutional 3D network is equal to 8.64 bpm, which is superior to 10 bpm for the other state-of-the-art methods. The robustness of the method to natural motion and increases in performance correspond to the two main avenues that will be considered in future works.

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“…Since this seminal work, there has been rapid growth in the literature pertaining to remote photoplethysmography (rPPG) techniques (e.g. [15], [16]). Interested reader may refer to recent reviews on rPPG [13], [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Remote heart rate variability for emotional state monitoring

Benezeth

Macwan

et al. 2018

2018 IEEE EMBS International Conference on Biomedical &Amp; Health Informatics (BHI)

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Abstract-Several researches have been conducted to recognize emotions using various modalities such as facial expressions, gestures, speech or physiological signals. Among all these modalities, physiological signals are especially interesting because they are mainly controlled by the autonomic nervous system. It has been shown for example that there is an undeniable relationship between emotional state and Heart Rate Variability (HRV). In this paper, we present a methodology to monitor emotional state from physiological signals acquired remotely. The method is based on a remote photoplethysmography (rPPG) algorithm that estimates remote Heart Rate Variability (rHRV) using a simple camera. We first show that the rHRV signal can be estimated with a high accuracy (more than 96% in frequency domain). Then, frequency-feature of rHRV is calculated and we show that there is a strong correlation between the rHRV feature and different emotional states. This observation has been validated on 12 out of 16 volunteers and video-induced emotions which opens the way to contactless monitoring of emotions from physiological signals.

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Unsupervised skin tissue segmentation for remote photoplethysmography

Abstract: International audienc

Cited by 355 publications

References 25 publications

Feasibility of assessing ultra-short-term pulse rate variability from video recordings

Feasibility of assessing ultra-short-term pulse rate variability from video recordings

3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video

Remote heart rate variability for emotional state monitoring

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