Video-Based Analysis of Heart Rate Applied to Falls

He, Xiaodong; Goubran, Rafik; Bennett, Stephanie L.; Robinovitch, Stephen N.; Symes, Bobbi; Lo, Bryan; Ejupi, Andreas; Knoefel, Frank

doi:10.1109/memea.2018.8438773

Cited by 6 publications

(6 citation statements)

References 18 publications

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“…The Video Magnification (VM) algorithm 57 – 59 can be used to magnify the color component of a video to detect subtle variations in color in a specific region of interest. When applied to the skin, it can estimate heart rate by quantifying the changes in skin color due to blood flow.…”

Section: Methodsmentioning

confidence: 99%

“…Given that our focus is to investigate FaceReader’s™ heart rate and pain expression estimations in relation to manual coding by experts, we selected video segments and skin regions with no movement to estimate the heart rate. The VM algorithm used in this study 58 , 73 , 74 has been verified through a comparison with wearable devices such as photoplethysmography (PPG). In the current study, in order to improve the reliability, the VM algorithm was applied to different areas of the body such as the face and hands.…”

Section: Methodsmentioning

confidence: 99%

“…We examined the validity and feasibility of FaceReader’s™ automated system by examining heart rate during painful situations in a video data set of older adult patients. In the current study, we assessed FaceReader’s™ heart rate estimations and pain related facial action coding against an established, but more labor intensive, optimized Video Magnification (VM) heart rate algorithm 57 – 59 and human annotated FACS data. Moreover, we investigated FaceReader’s™ performance by comparing the aggregate non-verbal scores calculated by FaceReader™ vs. trained coder coding.…”

Section: Introductionmentioning

confidence: 99%

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Automated vs. manual pain coding and heart rate estimations based on videos of older adults with and without dementia

Castillo

Browne

Hadjistavropoulos

et al. 2020

Journal of Rehabilitation and Assistive Technologies Engineerin

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Introduction Technological advances have allowed for the estimation of physiological indicators from video data. FaceReader™ is an automated facial analysis software that has been used widely in studies of facial expressions of emotion and was recently updated to allow for the estimation of heart rate (HR) using remote photoplethysmography (rPPG). We investigated FaceReader™-based heart rate and pain expression estimations in older adults in relation to manual coding by experts. Methods Using a video dataset of older adult patients with and without dementia, we assessed the relationship between FaceReader’s™ HR estimations against a well-established Video Magnification (VM) algorithm during baseline and pain conditions. Furthermore, we examined the correspondence between the Facial Action Coding System (FACS)-based pain scores obtained through FaceReader™ and manual coding. Results FaceReader’s™ HR estimations were correlated with VM algorithm in baseline and pain conditions. Non-verbal FaceReader™ pain scores and manual coding were also highly correlated despite discrepancies between the FaceReader™ and manual coding in the absolute value of scores based on pain-related facial action coding of the events preceding and following the pain response. Conclusions Compared to expert manual FACS coding and optimized VM algorithm, FaceReader™ showed good results in estimating HR values and non-verbal pain scores.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated vs. manual pain coding and heart rate estimations based on videos of older adults with and without dementia

Castillo

Browne

Hadjistavropoulos

et al. 2020

Journal of Rehabilitation and Assistive Technologies Engineerin

Self Cite

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“…There are many possible regions of interest (ROI) for heart rate detection with EVM including the forehead and cheeks on the face [31], the neck, and the wrist [30]. However, [38] showed that mean intensity values in a ROI on the forehead better correlated with the ECG signal than an ROI on the wrist.…”

Section: Limitations Of Contactless Heart Rate Measurement Through Videomentioning

confidence: 99%

Heart Rate Detection using Video Magnification: Impact of Algorithmic Parameters and Noise

Kassab¹

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Screening people for illnesses in public spaces using contactless techniques such as Video Magnification (VM) can be beneficial for both the crowd and the operators. One important vital sign to screen for is heart rate because it is indicative of the overall health of humans. In this thesis, first the impact of varying two input parameters (region of interest (ROI) size and window segment length) on VM is explored. Second, the impact of noise from three different sources (quantization, modern camera systems coupled with software enhancements, and light illumination) is explored.Results show that heart rate can be detected from very small ROIs, but that larger ROIs have higher accuracy. Larger window segment lengths are useful in the presence of motion. VM detects signals with amplitudes equal to one quantization level in 8-bit videos. VM is affected by modern camera systems, and it is more accurate with higher light illuminations.

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“…These changes are minuscule and cannot be detected by the naked eye because they are close to the noise levels for the video camera [25], [27], [28]. However, the HR associated with the color changes is correlated across the pixels within a skin region, and therefore, VM algorithms combine the pixels within a skin region and process the sequence of video frames to magnify the color variation and allow for the measurement the HR [25], [29].…”

Section: Motivationmentioning

confidence: 99%

A Confidence Framework for Heart Rate Estimation in Video Magnification

Elhajjar

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Parts of this thesis were published in "Assessing Confidence in Video Magnification Heart Rate Measurement using Multiple ROIs" [1] and "A Confidence Framework through Temporal Averaging for Heart Rate Estimation in Video Magnification" [2]. The above-stated references were presented at and are in conference proceedings for conferences by the Institute of Electrical and Electronics Engineers (IEEE). IEEE allows the reuse of published articles in a thesis or dissertation without requesting permission (see Declaration message from IEEE below). The material used from the above conference proceedings [1]-[2] will be both referenced in the following chapters as © 2023 [1] for [1] and © 2023 [2] for [2]. I am the lead author on both conference proceedings stated above.

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Video-Based Analysis of Heart Rate Applied to Falls

Cited by 6 publications

References 18 publications

Automated vs. manual pain coding and heart rate estimations based on videos of older adults with and without dementia

Automated vs. manual pain coding and heart rate estimations based on videos of older adults with and without dementia

Heart Rate Detection using Video Magnification: Impact of Algorithmic Parameters and Noise

A Confidence Framework for Heart Rate Estimation in Video Magnification

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