This article studies the relation between electrocardiogram (ECG) and photoplethysmogram (PPG) and investigates the inference of the ECG waveforms from the PPG signals that can be obtained from affordable wearable Internet-of-Things (IoT) devices for mobile health. In order to address this inverse problem, a transform is proposed to map the discrete cosine transform (DCT) coefficients of each PPG cycle to those of the corresponding ECG cycle based on the proposed cardiovascular signal model. The proposed method is evaluated with different morphologies of the PPG and ECG signals on three benchmark data sets with a variety of combinations of age, weight, and health conditions under several training setups. The experimental results show that the proposed method can achieve a high prediction accuracy greater than 0.92 in averaged correlation for each data set when the model is trained subjectwise. With a signal processing and learning system that is designed synergistically, we are able to reconstruct ECG signals by exploiting the relation of these two types of cardiovascular measurement. The reconstruction capability of the proposed method can enable low-cost ECG screening from affordable wearable IoT devices for continuous and long-term monitoring. This work opens up a new research direction to transfer the clinical ECG knowledge base to build a knowledge base for PPG and sensing data from wearable devices.

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.

This study segments social media publics and analyzes their informational behaviors during organizational crises. With a public-centric perspective, our study highlights how social media publics interact with each other (i.e., interdependence) and share different information through crisis stages (i.e., dynamics). Following the situational approach to segmentation, we identify different types of social media publics (i.e., influentials, broadcasters, and followers) based on their informational behaviors and their positions in an information sharing network. Crisis managers are recommended to pay more attention to publics with higher influence, namely key influentials and broadcasters. In addition, we try to understand social media publics’ changing concerns by analyzing whether and how publics share messages of different themes and forms in different stages of a crisis. Crisis managers are recommended to customize crisis communication content to fit publics’ needs, prioritize organizational resources, and maximize positive communication effect. With big data from Chipotle’s E. coli crisis, we analyzed the Twitter activities surrounding this crisis over a 6-month period. Our segmentation receives initial support from the network analysis and content analysis on the Twitter data, which lays the foundation for effective social media crisis management.

In the field of information forensics, many emerging problems involve a critical step that estimates and tracks weak frequency components in noisy signals. It is often challenging for the prior art of frequency tracking to i) achieve a high accuracy under noisy conditions, ii) detect and track multiple frequency components efficiently, or iii) strike a good trade-off of the processing delay versus the resilience and the accuracy of tracking. To address these issues, we propose Adaptive Multi-Trace Carving (AMTC), a unified approach for detecting and tracking one or more subtle frequency components under very low signal-to-noise ratio (SNR) conditions and in near real time. AMTC takes as input a time-frequency representation of the system's preprocessing results (such as the spectrogram), and identifies frequency components through iterative dynamic programming and adaptive trace compensation. The proposed algorithm considers relatively high energy traces sustaining over a certain duration as an indicator of the presence of frequency/oscillation components of interest and track their time-varying trend. Extensive experiments using both synthetic data and real-world forensic data of power signatures and physiological monitoring reveal that the proposed method outperforms representative prior art under low SNR conditions, and can be implemented in near real-time settings. The proposed AMTC algorithm can empower the development of new information forensic technologies that harness very small signals.

The Electric Network Frequency (ENF) signal is an environmental signature that can be captured in audio and video recordings made in locations where there is electrical activity. This signal is influenced by the power grid in which the recording is made, and recent work has shown that it can be useful towards a number of forensics and security applications. Most of the previous work done on the ENF signature can be categorized into two groups: work that targets improving approaches to extract the ENF signal, and work that explores applications that the ENF signature can be used for. An under-studied area of ENF research is the factors that can affect the capture of ENF traces in media recordings. Indeed, not all recordings made in areas of electrical activity will carry prominent ENF traces, and the strengths by which the ENF traces are captured can vary from one recording to another. A thorough understanding of the factors that can affect the capture of ENF traces in recordings is essential to understanding the true applicability of ENF-based approaches and can help inform future related studies. In this paper, we carry out a study on such factors, with a focus on audio signals, where we provide an overview of factors discussed earlier in the literature and examine new ones. We conclude with outlining avenues for future work.

Blood oxygen saturation (SpO $_2$ ) is an important indicator forpulmonary and respiratory functionalities. Clinical findings on COVID-19 show that many patients had dangerously low blood oxygen levels not long before conditions worsened. It is therefore recommended, especially for the vulnerable population, to regularly monitor the blood oxygen level for precaution. Recent works have investigated how ubiquitous smartphone cameras can be used to infer SpO $_2$ . Most of these works are contact-based, requiring users to cover a phone’s camera and its nearby light source with a finger to capture reemitted light from the illuminated tissue. Contact-based methods may lead to skin irritation and sanitary concerns, especially during a pandemic. In this paper, we propose a noncontact method for SpO $_2$ monitoring using hand videos acquired by smartphones. Considering the optical broadband nature of the red (R), green (G), and blue (B) color channels of the smartphone cameras, we exploit all three channels of RGB sensing to distill the SpO $_2$ information beyond the traditional ratio-of-ratios (RoR) method that uses only two wavelengths. To further facilitate an accurate SpO $_2$ prediction, we design adaptive narrow bandpass filters based on accurately estimated heart rate to obtain the most cardiac-related AC component for each color channel. Experimental results show that our proposed blood oxygen estimation method can reach a mean absolute error of 1.26% when a pulse oximeter is used as a reference, outperforming the traditional RoR method by 25%.