Video Magnification Algorithm Research Articles

Predicting Depression Risk from Facial Video-Derived Heart Rate Estimates

Common mental disorder is caused due to depression. Medical study shows that heart rate is linked to depression. Heart rate can give early warning of potential depression. Heart rate could predict the risk of depression. This link helps diagnosis and treatment of mental health issues like depression. Heart rate of healthy person is 60-85 beats per minute. When a person is depressed, his heart rate is not in normal range. Heart rate of depressed human being is increased beyond 85 beats per minute. Depression can be predicted with a 90% accuracy by analyzing a person’s heart rate. Using Eulerian Video Magnification algorithm, it is possible to calculate heart rate of person from facial video. Which gives benefit that no need of physical contact with the person. In the proposed research Heart rate is calculated by inputting face videos. Questionnaire is formed that contains 32 questions useful for depression assessment. Real time video dataset is collected while asking depression questionnaire to the people of all age groups. Using Eulerian Video Magnification algorithm, facial video is amplified and heart rate is estimated. Based on range, dataset is labeled as depressed or not depressed. By applying machine learning algorithms like Decision Tree (DT), Support Vector Machine (SVM) and Random forest (RF), dataset is classified with accuracy ranging from 96% to 100%. The performance of this research when compared with related work carried out for same research purpose, it is observed that accuracy obtained for this research work is 51% to 85.7% till date. This research gives more accurate model with new approach to predict risk of depression using heart rate estimated from facial videos.

The estimation of broiler respiration rate based on the semantic segmentation and video amplification

Respiratory rate is an indicator of a broilers’ stress and health status, thus, it is essential to detect respiratory rate contactless and stress-freely. This study proposed an estimation method of broiler respiratory rate by deep learning and machine vision. Experiments were performed at New Hope (Shandong Province, P. R. China) and Wen’s group (Guangdong Province, P. R. China), and a total of 300 min of video data were collected. By separating video frames, a data set of 3,000 images was made, and two semantic segmentation models were trained. The single-channel Euler video magnification algorithm was used to amplify the belly fluctuation of the broiler, which saved 55% operation time compared with the traditional Eulerian video magnification algorithm. The contour features significantly related to respiration were used to obtain the signals that could estimate broilers’ respiratory rate. Detrending and band-pass filtering eliminated the influence of broiler posture conversion and motion on the signal. The mean absolute error, root mean square error, average accuracy of the proposed respiratory rate estimation technique for broilers were 3.72%, 16.92%, and 92.19%, respectively.

Detection of the respiratory rate of standing cows by combining the Deeplab V3+ semantic segmentation model with the phase-based video magnification algorithm

The respiratory rate (RR) of dairy cows is closely related to heat stress and reproductive diseases. In this study, a method was proposed to detect the RR of standing resting dairy cows by using deep learning and video analysis. First, a Deeplab V3+ semantic segmentation model was developed using the framework of ResNet-101, and the model was trained and tested using 3,000 manually labelled images to realise the segmentation of the cows. Next, a phase-based video magnification (PBVM) algorithm was applied to amplify the weak breathing movements of the cow. Finally, the Lucas-Kanade (LK) optical flow algorithm was used to detect breathing direction, and an RR detection model was used to detect the RR. The effectiveness of the method was verified by testing a total of 67,812 frames of data collected from 70 video segments of different cows. A comparison with the original video LK optical flow method (Method 1), target segmentation video LK optical flow method (Method 2), and original video motion amplification LK optical flow method (Method 3) showed that the accuracy of the proposed method was between 80.00% and 100.00%, and the mean accuracy was 93.04%. These values were 45.82%, 17.72%, and 22.94% higher than those of the other three methods, respectively. In addition, the method was tested in 105 videos of cows under heat stressed, and the RR detection accuracy was 98.69%. The results showed that the method could provide a reference for the automatic detection of RR and remote diagnosis of respiratory-related diseases.

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

IntroductionTechnological 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.MethodsUsing 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.ResultsFaceReader’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.ConclusionsCompared to expert manual FACS coding and optimized VM algorithm, FaceReader™ showed good results in estimating HR values and non-verbal pain scores.

Visualization process assisted by the Eulerian video magnification algorithm for a heart rate monitoring system: mobile applications

Medical technology employs a range of different computational methods for analyzing noninvasive heart rate monitoring processes. During the heart rate monitoring process, accurate heart rate detection is difficult which is overcome by applying the effective image processing techniques. Thus, the present study offers a mobile application framework that uses video frames captured from mobile camera. These video frames are analyzed by Eulerian video magnification (EVM) with a region extraction technique for efficiently detecting and monitoring heart rates. Initially, EVM captures the feelings of the subject from a face visualization process. Then, temporal filtering and spatial decomposition methods are applied to reconstruct the video frames. Subtle changes in the face image are extracted from the video frames by applying an enhanced multi-scale segmentation technique. The segmented region from the face helps to identify the intensity level of the green channel feature, which differs from the normal red and blue channel features. This process identifies the hidden heart rate from the region extracted by the EVM visualization process. Finally, system efficiency is evaluated in terms of the estimated heart rate, actual heart rate, and heart rate accuracy. The mobile based video magnification analyzing process reduces mortality rate, and accurately examines heart rates.

Soot propensity by image magnification and artificial intelligence

This paper presents the results of two novel approaches to measure the soot propensity of a flame and their comparison with the Line of Sight Attenuation (LOSA) method. Both approaches are based on the detection of the Smoke Point Height (SPH), concept used to determine when a flame is in a sooting state. The first approach is based on the detection of morphological changes in the flame, identified through their amplification via the Eulerian Video Magnification algorithm. Results show an effective amplification of the flame geometry, allowing the visualization of variations on the flame tip unable to be detected by the naked human eye and therefore the detection of SPH. The second approach is based on the application of Artificial Intelligence models to classify flame images regarding their sooting propensity, taking advantage of the knowledge acquired from a referential data set. Both approaches provide an accurate classification when compared to the conventional method of LOSA. Furthermore, both approaches show a greater implementation potential in practical combustion devices than the conventional method of LOSA, due to their reduced hardware and technical requirements.

High speed in-vivo imaging of retinal hemodynamics in a rodent model of hypertension.

The eye is the only organ through which microcirculation can be visualized non-invasively. This unique feature makes the eye and specifically retinal vasculature an excellent target area to monitor and study micro-vascular damage in systemic diseases. Dynamic (real-time) changes of retinal vessels have been shown to be more specific to the disease in comparison with static measurements. In this study we utilize high speed imaging (i.e. 125 fps) to study and derive dynamic changes of retinal vessels in a rat model of hypertension. A Eulerian video magnification algorithm was used to extract retinal arterial and venous pulse amplitude from five Spontaneously Hypertensive Rats (SHR) and five Wister Kyoto (WKY) rats were used as the control group. Results showed that retinal arterial diameter and pulse amplitude are significantly lower in the SHRs compared with WKYs. Dynamic biomarkers of retinal micro-vasculature may be used as a diagnostic tool for systemic diseases.