Preparation of stable magnetic nanofluids containing Fe3O4@PPy nanoparticles by a novel one-pot route

Stable magnetic nanofluids containing Fe3O4@Polypyrrole (PPy) nanoparticles (NPs) were prepared by using a facile and novel method, in which one-pot route was used. FeCl3·6H2O was applied as the iron source, and the oxidizing agent to produce PPy. Trisodium citrate (Na3cit) was used as the reducing reagent to form Fe3O4 NPs. The as-prepared nanofluid can keep long-term stability. The Fe3O4@PPy NPs can still keep dispersing well after the nanofluid has been standing for 1 month and no sedimentation is found. The polymerization reaction of the pyrrole monomers took place with Fe3+ ions as the initiator, in which these Fe3+ ions remained in the solution adsorbed on the surface of the Fe3O4 NPs. Thus, the core-shell NPs of Fe3O4@PPy were obtained. The particle size of the as-prepared Fe3O4@PPy can be easily controlled from 7 to 30 nm by the polymerization reaction of the pyrrole monomers. The steric stabilization and weight of the NPs affect the stability of the nanofluids. The as-prepared Fe3O4@PPy NPs exhibit superparamagnetic behavior

Contrast-Phys:unsupervised video-based remote physiological measurement via spatiotemporal contrast

Abstract Video-based remote physiological measurement utilizes face videos to measure the blood volume change signal, which is also called remote photoplethysmography (rPPG). Supervised methods for rPPG measurements achieve state-of-the-art performance. However, supervised rPPG methods require face videos and ground truth physiological signals for model training. In this paper, we propose an unsupervised rPPG measurement method that does not require ground truth signals for training. We use a 3DCNN model to generate multiple rPPG signals from each video in different spatiotemporal locations and train the model with a contrastive loss where rPPG signals from the same video are pulled together while those from different videos are pushed away. We test on five public datasets, including RGB videos and NIR videos. The results show that our method outperforms the previous unsupervised baseline and achieves accuracies very close to the current best supervised rPPG methods on all five datasets. Furthermore, we also demonstrate that our approach can run at a much faster speed and is more robust to noises than the previous unsupervised baseline. Our code is available at https://github.com/zhaodongsun/contrast-phys

Privacy-Phys:facial video-based physiological modification for privacy protection

Abstract The invisible remote photoplethysmography (rPPG) signals in facial videos can reveal the cardiac rhythm and physiological status. Recent studies show that rPPG is a non-contact way for emotion recognition, disease detection, and biometric identification, which means there is a potential privacy problem about physiological information leakage from facial videos. Therefore, it is essential to process facial videos to prevent rPPG extraction in privacy-sensitive situations such as online video meetings. In this letter, we propose Privacy-Phys, a novel method based on a pre-trained 3D convolutional neural network, to modify rPPG in facial videos for privacy protection. Our experimental results show that our approach can modify rPPG signals in facial videos more effectively and efficiently than the previous baseline. Our method can be applied to process facial videos in online video meetings or video-sharing platforms to prevent rPPG from being captured maliciously

Non-contact atrial fibrillation detection from face videos by learning systolic peaks

Abstract Objective: We propose a non-contact approach for atrial fibrillation (AF) detection from face videos. Methods: Our proposed method can accurately extract systolic peaks from face videos for AF detection. The proposed method is trained with subject-independent 10-fold cross-validation with 30s video clips and tested on two tasks. 1) Classification of healthy versus AF: the accuracy, sensitivity, and specificity are 96.00%, 95.36%, and 96.12%. 2) Classification of SR versus AF: the accuracy, sensitivity, and specificity are 95.23%, 98.53%, and 91.12%. In addition, we also demonstrate the feasibility of non-contact AFL detection. Conclusion: We achieve good performance of non-contact AF detection by learning systolic peaks. Significance: non-contact AF detection can be used for self-screening of AF symptoms for suspectable populations at home or self-monitoring of AF recurrence after treatment for chronic patients

The 2nd challenge on remote physiological signal sensing (RePSS)

Abstract Remote measurement of physiological signals from videos is an emerging topic. The topic draws great interest, but the lack of publicly available benchmark databases and a fair validation platform are hindering its further development. The RePSS Challenge is organized as an annual event for this concern. Here the 2nd RePSS is organized in conjunction with ICCV 2021. The 2nd RePSS contains two competition tracks. Track 1 is to measure inter-beat-intervals (IBI) from facial videos, which requires accurate measurement of each individual pulse peak. Track 2 is about respiration measurement from facial videos, as respiration is another important physiological index related to both health and emotional status. One new dataset is built and shared for Track 2. This paper presents an overview of the challenge, including data, protocol, results, and discussion. We highlighted the top-ranked solutions to provide insight for researchers, and we also outline future directions for this topic and this challenge

Estimating stress in online meetings by remote physiological signal and behavioral features

Abstract Work stress impacts people’s daily lives. Their well-being can be improved if the stress is monitored and addressed in time. Attaching physiological sensors are used for such stress monitoring and analysis. Such approach is feasible only when the person is physically presented. Due to the transfer of the life from offline to online, caused by the COVID-19 pandemic, remote stress measurement is of high importance. This study investigated the feasibility of estimating participants’ stress levels based on remote physiological signal features (rPPG) and behavioral features (facial expression and motion) obtained from facial videos recorded during online video meetings. Remote physiological signal features provided higher accuracy of stress estimation (78.75%) as compared to those based on motion (70.00%) and facial expression (73.75%) features. Moreover, the fusion of behavioral and remote physiological signal features increased the accuracy of stress estimation up to 82.50%