Analyzing Trend for U.S. Immigrants' e-Health Engagement from 2008 to 2013.

Our study examines the trend of U.S. immigrants' engagement in various e-health activities and how immigrants' use of e-health technologies is associated with their cultural characteristics over years. Aggregating three national representative samples of U.S. immigrants from the Health Information National Trends Survey (HINTS) conducted between 2008 and 2013, our results from trend analyses and logistic regressions revealed that the U.S. immigrants exhibited an increasing trend on two types of e-health engagement: tracking personal health information online and communicating with a doctor online. But we did not find any significant change in the other two e-health activities throughout these years: the percentage of immigrants who sought a health provider online remained moderate (about 37%), whereas the percentage of immigrants who joined an online support group kept low (about 5%). Furthermore, immigrants of different race/ethnicity or length of residence in the United States showed different trends of e-health engagement. Asian immigrants and immigrants with 13-34 years of residence were more proactive to engage in various e-health activities. Our study informs the practice aiming to enhance U.S. immigrants' Internet use for better health-related outcomes.

Analysis of Coding Gain Due to In-Loop Reshaping.

Reshaping, a point operation that alters the characteristics of signals, has been shown capable of improving the compression ratio in video coding practices. Out-of-loop reshaping that directly modifies the input video signal was first adopted as the supplemental enhancement information (SEI) for the HEVC/H.265 without the need to alter the core design of the video codec. VVC/H.266 further improves the coding efficiency by adopting in-loop reshaping that modifies the residual signal being processed in the hybrid coding loop. In this paper, we theoretically analyze the rate-distortion performance of the in-loop reshaping and use experiments to verify the theoretical result. We prove that the in-loop reshaping can improve coding efficiency when the entropy coder adopted in the coding pipeline is suboptimal, which is in line with the practical scenarios that video codecs operate in. We derive the PSNR gain in a closed form and show that the theoretically predicted gain is consistent with that measured from experiments using standard testing video sequences.

A Comparative Study of Principled rPPG-Based Pulse Rate Tracking Algorithms for Fitness Activities.

Performance improvements obtained by recent principled approaches for pulse rate (PR) estimation have typically been achieved by adding or modifying certain modules within a reconfigurable system. Yet, evaluations are usually performed only at the system level. To better understand each module's contribution and facilitate future research in explainable learning and artificial intelligence for physiological monitoring, this paper conducts a comparative study of video-based, principled PR tracking algorithms, with a particular focus on challenging fitness scenarios. A review of the progress achieved over the last decade and a half in this field is utilized to construct the major processing modules of a reconfigurable remote pulse rate sensing system. Experiments are conducted on two challenging datasets-an internal collection of 25 videos of two Asian males exercising on stationary-bike, elliptical, and treadmill machines and 34 videos from a public ECG fitness database of 14 men and 3 women exercising on elliptical and stationary-bike machines. The signal-to-noise ratio (SNR), Pearson's correlation coefficient, error count ratio, error rate, and root mean squared error are used for performance evaluation. The top-performing configuration produces respective values of -0.8 dB, 0.86, 9%, 1.7%, and 3.3 beats per minute (bpm) for the internal dataset and 1.3 dB, 0.77, 28.6%, 6.0%, and 8.1 bpm for the ECG Fitness dataset, achieving significant improvements over alternative configurations. Our results indicate a synergistic effect between pulse color mapping and adaptive motion filtering, as well as the importance of a robust frequency tracking algorithm for PR estimation in low SNR settings.

Remote Blood Oxygen Estimation From Videos Using Neural Networks.

Peripheral blood oxygen saturation (SpO 2) is an essential indicator of respiratory functionality and received increasing attention during the COVID-19 pandemic. Clinical findings show that COVID-19 patients can have significantly low SpO 2 before any obvious symptoms. Measuring an individual's SpO 2 without having to come into contact with the person can lower the risk of cross contamination and blood circulation problems. The prevalence of smartphones has motivated researchers to investigate methods for monitoring SpO 2 using smartphone cameras. Most prior schemes involving smartphones are contact-based: They require using a fingertip to cover the phone's camera and the nearby light source to capture reemitted light from the illuminated tissue. In this paper, we propose the first convolutional neural network based noncontact SpO 2 estimation scheme using smartphone cameras. The scheme analyzes the videos of an individual's hand for physiological sensing, which is convenient and comfortable for users and can protect their privacy and allow for keeping face masks on. We design explainable neural network architectures inspired by the optophysiological models for SpO 2 measurement and demonstrate the explainability by visualizing the weights for channel combination. Our proposed models outperform the state-of-the-art model that is designed for contact-based SpO 2 measurement, showing the potential of the proposed method to contribute to public health. We also analyze the impact of skin type and the side of a hand on SpO 2 estimation performance.

Federated Learning via Plurality Vote.

Federated learning allows collaborative clients to solve a machine-learning problem while preserving data privacy. Recent studies have tackled various challenges in federated learning, but the joint optimization of communication overhead, learning reliability, and deployment efficiency is still an open problem. To this end, we propose a new scheme named federated learning via plurality vote (FedVote). In each communication round of FedVote, clients transmit binary or ternary weights to the server with low communication overhead. The model parameters are aggregated via weighted voting to enhance the resilience against Byzantine attacks. When deployed for inference, the model with binary or ternary weights is resource-friendly to edge devices. Our results demonstrate that the proposed method can reduce quantization error and converges faster compared to the methods directly quantizing the model updates.

Analysis of dyadic approximation error for hybrid video codecs with integer transforms.

In this paper, we present an analysis of the dyadic approximation error introduced by the integerization of transform coding in H.264/AVC-like codecs. We derive the analytical formulations for dyadic approximation error and nonorthogonality error. We further classify the dyadic approximation error into a "system error" and a "nonflat error," and proposed two models for them. We found that the "nonflat error" has a substantial impact on video quality if the number of shifting bits at decoder side (DQ_BITS) is small. We also give a theoretical justification on why scaling factors at encoder side are better to be adapted to the rescaling factors at decoder side in H.264/AVC-like codecs.