Bounded Kalman filter method for motion-robust, non-contact heart rate estimation

Prakash, Sakthi Kumar Arul; Tucker, Conrad S.

doi:10.1364/boe.9.000873

Cited by 49 publications

(17 citation statements)

References 58 publications

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“…The current research methods focus on illumination [ 16 , 20 ], motion [ 20 , 21 , 22 ], video compression [ 19 ], and other aspects, but less attention is paid to the selection of regions of interest, the impact of face detection methods, and the datasets suitable for heart rate detection.…”

Section: Related Workmentioning

confidence: 99%

“…At present, rPPG is tentatively applied in Intensive Care Units (ICU), because the subjects are still and a frontal face video can be continuously collected. Under these ideal conditions, the root mean square error (RMSE) of the traditional non-contact heart rate measurement method is between 1.24 and 8.90 bpm [ 16 , 27 , 39 , 40 ]. The frontal face RMSE of our proposed method reaches 3.49 bpm, which is very close to state-of-the-art traditional methods.…”

Section: Related Workmentioning

confidence: 99%

“…Poh et al [ 13 ] proposed a blind source separation method to reduce the error caused by motion. In addition, there are some other methods to solve the problem of motion artifacts, such as Chrominance (CHROM) [ 14 ], wavelet transform [ 15 ], and bounded Kalman filter technology [ 16 ], etc.…”

Section: Introductionmentioning

confidence: 99%

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Non-Contact Heart Rate Detection When Face Information Is Missing during Online Learning

Zheng

Cui

et al. 2020

Sensors

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Research shows that physiological signals can provide objective data support for the analysis of human emotions. At present, non-contact heart rate data have been employed in the research of medicine, intelligent transportation, smart education, etc. However, it is hard to detect heart rate data using non-contact traditional methods during head rotation, especially when face information is missing in scenarios such as online teaching/learning. Traditional remote photoplethysmography (rPPG) methods require a static, full frontal face within a fixed distance for heart rate detection. These strict requirements make it impractical to measure heart rate data in real-world scenarios, as a lot of videos only partially record the subjects’ face information, such as profile, too small distance, and wearing a mask. The current algorithm aims to solve the problem of head deflections between 30 degrees and 45 degrees by employing a symmetry substitution method, which can replace the undetected region of interest (ROI) with the detectable one. When face information is partially missing, our algorithm uses face–eye location to determine ROI. The results show that the method in this paper can solve certain practical problems related to heart rate detection, with a root mean square error (RMSE) under 7.64 bpm.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Non-Contact Heart Rate Detection When Face Information Is Missing during Online Learning

Zheng

Cui

et al. 2020

Sensors

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“…Furthermore, the multi view human pose estimation process is described in [20][21][22]. For determining heart rate, 2 Motion Capture Systems Using Optimal Signal Processing Algorithm: A State-of-the-art Literature a KF algorithm is used in [23,24]. It has a significant role not only in the medical science but also in weather forecasting [25].…”

Section: Literature Reviewmentioning

confidence: 99%

Motion Capture Systems Using Optimal Signal Processing Algorithm: A State-of-the-art Literature

Rana¹,

Halim²

2018

ujcn

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Motion capture system is a promising research area but the availability of the state-of-the-art literature review on this topic is limited. This research provides a comprehensive literature review for motion capture systems with Kalman filtering algorithms. The summarized state-of-the-art literature review of motion capturing system is being analyzed considering the advantages and the disadvantages of motion capture systems using Kalman filtering algorithm. Afterwards, the human body incorporating noises is represented by system of equations where the system states are unknown. The Kalman filter is adopted to estimate the human body orientations. Simulation results show that the proposed algorithm can be used to track human body orientation accurately.

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“…Partial demographic analysis (across skin tones) was performed in this study. The VideoVitals approach developed by Prakash and Tucker employs a bounded Kalman Filter (BKF) and a denoising algorithm to reduce the effect of motion and improve feature tracking 34 . The BKF algorithm is tested on subjects performing a range of head motions and is shown to outperform the existing methods for reducing motion inaccuracies.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of biases in remote photoplethysmography methods

et al. 2021

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This work investigates the estimation biases of remote photoplethysmography (rPPG) methods for pulse rate measurement across diverse demographics. Advances in photoplethysmography (PPG) and rPPG methods have enabled the development of contact and noncontact approaches for continuous monitoring and collection of patient health data. The contagious nature of viruses such as COVID-19 warrants noncontact methods for physiological signal estimation. However, these approaches are subject to estimation biases due to variations in environmental conditions and subject demographics. The performance of contact-based wearable sensors has been evaluated, using off-the-shelf devices across demographics. However, the measurement uncertainty of rPPG methods that estimate pulse rate has not been sufficiently tested across diverse demographic populations or environments. Quantifying the efficacy of rPPG methods in real-world conditions is critical in determining their potential viability as health monitoring solutions. Currently, publicly available face datasets accompanied by physiological measurements are typically captured in controlled laboratory settings, lacking diversity in subject skin tones, age, and cultural artifacts (e.g, bindi worn by Indian women). In this study, we collect pulse rate and facial video data from human subjects in India and Sierra Leone, in order to quantify the uncertainty in noncontact pulse rate estimation methods. The video data are used to estimate pulse rate using state-of-the-art rPPG camera-based methods, and compared against ground truth measurements captured using an FDA-approved contact-based pulse rate measurement device. Our study reveals that rPPG methods exhibit similar biases when compared with a contact-based device across demographic groups and environmental conditions. The mean difference between pulse rates measured by rPPG methods and the ground truth is found to be ~2% (1 beats per minute (b.p.m.)), signifying agreement of rPPG methods with the ground truth. We also find that rPPG methods show pulse rate variability of ~15% (11 b.p.m.), as compared to the ground truth. We investigate factors impacting rPPG methods and discuss solutions aimed at mitigating variance.

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Bounded Kalman filter method for motion-robust, non-contact heart rate estimation

Cited by 49 publications

References 58 publications

Non-Contact Heart Rate Detection When Face Information Is Missing during Online Learning

Non-Contact Heart Rate Detection When Face Information Is Missing during Online Learning

Motion Capture Systems Using Optimal Signal Processing Algorithm: A State-of-the-art Literature

Evaluation of biases in remote photoplethysmography methods

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