Robust Heart Rate Measurement from Video Using Select Random Patches

Lam, Antony; Kuno, Yoshinori

doi:10.1109/iccv.2015.415

Cited by 144 publications

(83 citation statements)

References 19 publications

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“…The benchmarking of the methods using MANHOB-HCI database shows that the results reported in our experiment (see Table 3) mainly the RMSE were similar to the results reported in [26,30,31], therefore, demonstrating that we have correctly reimplemented the state of the art method. Measured heart rate of each of the methods was validated with the actual heart rate.…”

Section: Benchmarkingsupporting

confidence: 81%

Towards health monitoring using remote heart rate measurement using digital camera: A feasibility study

et al. 2020

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a b s t r a c tThe paper presents a feasibility study for heart rate measurement using a digital camera to perform health monitoring. The feasibility study investigates the reliability of the state of the art heart rate measuring methods in realistic situations. Therefore, an experiment was designed and carried out on 45 subjects to investigate the effects caused by illumination, motion, skin tone, and distance variance. The experiment was conducted for two main scenarios; human-computer interaction scenario and health monitoring scenario. The human-computer scenario investigated the effects caused by illumination variance, motion variance, and skin tone variance. The health monitoring scenario investigates the feasibility of health monitoring at public spaces (i.e. airports, subways, malls). Five state of the art heart rate measuring methods were re-implemented and tested with the feasibility study database. The results were compared with ground truth to estimate the heart rate measurement error. The heart rate measurement error was analyzed using mean error, standard deviation; root means square error and Pearson correlation coefficient. The findings of this experiment inferred promising results for health monitoring of subjects standing at a distance of 500 cm.

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Section: Benchmarkingsupporting

confidence: 81%

Towards health monitoring using remote heart rate measurement using digital camera: A feasibility study

et al. 2020

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“…Video-based physiological measurement involves capturing both subtle color changes (iPPG) and small motions (iBCG and respiratory movement) of the human body using a camera. For modeling lighting, imagers and physiology, previous works used the Lambert-Beer law (LBL) [16,42] or Shafer's dichromatic reflection model (DRM) [39]. We build our learning model on top of the DRM as it provides a better framework for modeling both color changes and motions.…”

Section: Skin Reflection Modelmentioning

confidence: 99%

DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks

Chen

McDuff

2018

Lecture Notes in Computer Science

364

345

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Non-contact video-based physiological measurement has many applications in health care and human-computer interaction. Practical applications require measurements to be accurate even in the presence of large head rotations. We propose the first end-to-end system for videobased measurement of heart and breathing rate using a deep convolutional network. The system features a new motion representation based on a skin reflection model and a new attention mechanism using appearance information to guide motion estimation, both of which enable robust measurement under heterogeneous lighting and major motions. Our approach significantly outperforms all current state-of-the-art methods on both RGB and infrared video datasets. Furthermore, it allows spatial-temporal distributions of physiological signals to be visualized via the attention mechanism.

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“…We employed the pose-free facial landmark fitting tracker for face detection and tracking [8]. This tracker has been employed by previous HR estimation methods and can simultaneously handle face detection, pose-free landmark localization and tracking over a large range of motions in real time [7]. Following face detection and tracking, a feature point recovery system has been devised to overcome extreme motion artifacts.…”

Section: Recovery Of Feature Tracking Pointsmentioning

confidence: 99%

“…Hence, can be modeled as: (6) From eqns. 5 and 6, we have: (7) Rather that solely looking at eqn.7, we have focused on eq.5 and tried to minimize the error . Once the optimal K is estimated, the reasonable approximation for can be computed.…”

Section: Illumination Rectificationmentioning

confidence: 99%

Spectral Reflectance Based Heart Rate Measurement from Facial Video

Subramaniam

Rajitha

2019

2019 IEEE International Conference on Image Processing (ICIP)

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Remote detection of the cardiac pulse has a number of applications in sports and medicine, and can be used to determine an individual's physiological state. Previous approaches to estimate Heart Rate (HR) from video require the subject to remain stationary and employ background information to eliminate illumination interferences. The present research proposes a spectral reflectance-based novel illumination rectification method to eliminate illumination variations in the video. Our method does not rely on the background of the video and is robust to extreme motion interferences (head movements). Furthermore, in order to tackle extreme motion artifacts, the present framework introduces a novel feature point recovery system which recovers the feature tracking points lost during extreme head movements of the subject. Finally, the individual HR estimates from multiple feature points are combined to produce an average HR. We evaluate the efficacy of our framework on the MAHNOB-HCI dataset, a publicly available dataset employed by previous methods. Our HR measurement framework outperformed previous methods and had a root mean square error (RMSE) of 5.21%.

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Robust Heart Rate Measurement from Video Using Select Random Patches

Cited by 144 publications

References 19 publications

Towards health monitoring using remote heart rate measurement using digital camera: A feasibility study

Towards health monitoring using remote heart rate measurement using digital camera: A feasibility study

DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks

Spectral Reflectance Based Heart Rate Measurement from Facial Video

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