Monitoring Engagement in Online Classes Through WiFi CSI

Due to the Covid-19 pandemic, people have been forced to move to online spaces to attend classes or meetings and so on. The effectiveness of online classes depends on the engagement level of students. A straightforward way to monitor the engagement is to observe students' facial expressions, eye gazes, head gesticulations, hand movements, and body movements through their video feed. However, video-based engagement detection has limitations, such as being influenced by video backgrounds, lighting conditions, camera angles, unwillingness to open the camera, etc. In this work, we propose a non-intrusive mechanism of estimating engagement level by monitoring the head gesticulations through channel state information (CSI) of WiFi signals. First, we conduct an anonymous survey to investigate whether the head gesticulation pattern is correlated with engagement. We then develop models to recognize head gesticulations through CSI. Later, we plan to correlate the head gesticulation pattern with the instructor's intent to estimate the students' engagement. © 2023 IEEE.

CSI-Based Joint Location and Activity Monitoring for COVID-19 Quarantine Environments

The recent SARS-COV-2 virus, also known as COVID-19, badly affected the world's healthcare system due to limited medical resources for a large number of infected human beings. Quarantine helps in breaking the spread of the virus for such communicable diseases. This work proposes a nonwearable/contactless system for human location and activity recognition using ubiquitous wireless signals. The proposed method utilizes the channel state information (CSI) of the wireless signals recorded through a low-cost device for estimating the location and activity of the person under quarantine. We propose to utilize a Siamese architecture with combined one-dimensional convolutional neural networks (1-D-CNNs) and bi-directional long short-term memory (Bi-LSTM) networks. The proposed method provides high accuracy for the joint task and is validated on two real-world testbeds, first, using the designed low-cost CSI recording hardware, and second, on a public dataset for joint activity and location estimation. The human activity recognition (HAR) results outperform state-of-the-art machine and deep learning methods, and localization results are comparable with the existing methods. © 2001-2012 IEEE.

COVID-19 Severity Is Associated with Selenium Intake among Young Adults with Low Selenium and Zinc Intake in North Carolina.

Background: The effects of coronavirus disease 2019 (COVID-19) remain a global public health emergency because of the ensuing economic burden and death. With robust research into vaccines, antibody treatments, and antiviral drugs for COVID-19, there is still a dearth of evidence on the role of an individual's nutritional status on the severity of COVID-19. Objective: This study aimed to investigate the association between selenium (Se) and zinc (Zn) status and COVID-19 severity among individuals diagnosed with COVID-19 in North Carolina. Methods: Subjects (n = 106) were recruited remotely as part of the Nutrition and COVID-19 in North Carolina (NC-NC) study and filled out online screening questionnaires and dietary surveys. Toenail samples from 97 participants were analyzed to determine Se and Zn concentrations. To assess the severity of severe acute respiratory coronavirus (SARS-CoV)-2 infection, subjects were asked about the presence and duration of 10 commonly reported symptoms. These responses were used to calculate a COVID-19 severity index (CSI). The relationship between Se and Zn status (intake and toenail concentrations) and CSI was explored using a regression analysis. Results: Our results showed that the median (25th, 75th percentiles) dietary Se and Zn intake from selected food sources were 65.2 µg (43.2, 112.9) and 4.3 mg (1.8, 8), respectively. Headache, cough, loss of smell or taste, and fever were reported by at least half of the participants. In stepwise regression analysis, among individuals with low Se and Zn intake (below the median), Se intake was inversely associated with increasing CSI (ß = -0.66; 95% CI: -1.21, -0.11; P = 0.02). Conclusions: Findings from this study support a potential benefit of increasing the intake of dietary Se to mitigate the severity of SARS-CoV-2 infection.

Covid-19 contact tracing through multipath profile similarity

Contact tracing is a key approach to control the spread of Covid-19 and any other pandemia. Recent attempts have followed either traditional ways of tracing (e.g. patient interviews) or unreliable app-based localization solutions. The latter has raised both privacy concerns and low precision in the contact inference. In this work, we present the idea of contact tracing through the multipath profile similarity. At first, we collect Channel State Information (CSI) traces from mobile devices, and then we estimate the multipath profile. We then show that positions that are close obtain similar multipath profiles, and only this information is shared outside the local network. This result can be applied for deploying a privacy-preserving contact tracing system for healthcare authorities. © 2022 Owner/Author.

The Integration of Transportation Modes for Bromo-Tengger-Semeru National Park

Introduction: The spread of the COVID-19 virus requires unprecedented steps from the government such as the restriction of travelers and activities and enforcement of social distancing to reduce interaction between individuals. This led to a drastic decrease in tourist visits to the TN-BTS between January and December 2020 with the area reported having been closed between April-July. Meanwhile, there was an inadequate transportation system at a tourist destination area before the pandemic and that made the movement more difficult during the pandemic. Therefore, this research is focused on describing transportation modes integration in the East Java-Indonesia TN-BTS area during this period. Methods: The intermodal integration was represented through the community's preference which was based on certain elements such as the need for connecting mode, main mode, multimodal network, transitional facilities, switching facilities with different networks, schedules integration, tariffs, routes, and times. Results and Discussion: The survey conducted, however, showed these indicators are below expectation as observed with the community satisfaction with transportation integration in the TN-BTS area measured to be very low, below 60%, using the Customer Satisfaction Index (CSI) tool. Moreover, the expectations on the variables for intermodal integration were also measured using Important Performance Analysis (IPA). Conclusion: The findings showed the policymakers and planners need to take steps to encourage the realization of transportation integration in the TN-BTS area to develop tourism in the area. © 2022 Priyambodo et al.

CSI-based Joint Location and Activity Monitoring for COVID-19 Quarantine Environments

The recent SARS-COV-2 virus, also known as COVID-19, badly affected the world’s healthcare system due to limited medical resources for a large number of infected human beings. Quarantine helps in breaking the spread of the virus for such communicable diseases. This work proposes a non-wearable/contactless system for human location and activity recognition using ubiquitous wireless signals. The proposed method utilizes the Channel State Information (CSI) of the wireless signals recorded through a low-cost device for estimating the location and activity of the person under quarantine. We propose to utilize a Siamese architecture with combined one-dimensional Convolutional Neural Networks (1D-CNN) and Bi-directional long-short term memory (Bi-LSTM) networks. The proposed method provides high accuracy for the joint task and is validated on two real-world testbeds. First, using the designed low-cost CSI recording hardware, and second, on a public dataset for joint activity and location estimation. The HAR results outperform state-of-the-art machine and deep learning methods, and localization results are comparable with the existing methods. IEEE

"the Errors Are Egregious": Assessing the Csi Effect and Undergraduate Students' Perceptions of Forensic Science through a Pre- and Post-Test Investigation

Purpose: The authors attempt to capture new forensic science students' preconceptions of the field and their assessment of competencies. Methodology: The authors surveyed students at a Historically Black College and University and a Primarily White Institution on their viewership of crime and forensic TV shows and measured their competencies in a range of forensic science skills at the start and end of the semester, along with having students capture errors and evidence from an episode of CSI Las Vegas. Findings: Students who were viewers of crime series with and without prior forensics coursework over evaluated their level of preparedness at the start of the semester, often ranking themselves as moderately or well prepared in blood spatter analysis, fingerprinting, bodily fluid, and hair/ fiber collection. Research limitations: The authors relied on a convenience sample of forensic science courses, and their comparison of student learning was disrupted by COVID-19. Originality: The authors examine student concerns with working at crime scenes and reflections on their abilities to succeed in the field. The authors discuss the need for incorporating media literacy, content warnings, and emotional socialization and professional development into forensic science curricula to better equip and prepare students for careers as crime scene investigators and forensic analysts.

A survey on vital signs monitoring based on Wi-Fi CSI data.

The COVID-19 pandemic further highlighted the need to use low-cost remote monitoring procedures for medical patients. Since the results reported in the literature have shown that the use of Channel State Information (CSI) from Wi-Fi networks to remotely monitor patients can provide means to obtain a powerful medical information package in a non-invasive way and at low cost, a consistent review and analysis of the state of the art on this applied technique is developed in the present work. Initially, a mathematical overview of the CSI technology and its functional model is done. Subsequently, details about the technical approach necessary to use CSI in medical applications and a summary of the studies reported in the literature with such applications are presented. Based on the analyses and discussions carried out throughout this work, a better understanding of the current state of the art is achieved. Challenges and perspectives for future research are also highlighted.

WiHead: WiFi-Based Head-Pose Estimation

Due to the impact of Covid-19, people have started to conduct online courses or meetings. However, this makes it difficult to communicate with each other effectively because of the lack of non-verbal communication. Although webcams are available for online courses, etc., people often do not want to turn them on for privacy reasons. Thus, there is a need to develop privacy preserving way to enable non-verbal communication in online learning and work environments. WiFi as a sensor can be used to detect non-verbal gestures such as head poses, and has been increasingly valued due to its advantages of avoiding the effects of light, non-line of sight monitoring, privacy protection, etc. In this paper, we proposed an approach, which uses WiFi CSI data to estimate head pose. Our approach not only use the amplitude and phase data of raw CSI data, but also use the information in frequency domain. Our experiment with proposed approach confirmed the feasibility of head pose estimation based on WiFi CSI data. This has important implications for device-free sensing detection. Especially in today’s world where web conferences and online courses are widely used, WiFi-based head recognition can give feedback to the other party while protecting privacy, which helps to improve the quality and comfort of communication. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-Beat: a low-cost breath monitoring approach for people in quarantine during the pandemic

Due to COVID-19, people have to adapt to the new lifestyle until scientists develop a permanent solution for this pandemic. Monitoring the respiration rate is very important for a COVID-infected person because the Coronavirus infects the pulmonary system of the person. Two problems that arise while monitoring the breath rate are: sensors are contact based and expensive for mass deployment. A conventional wearable breath rate monitoring system burdens the COVID-affected patient and exposes the caregivers to possible transmission. A contactless low-cost breath monitoring system is required, which monitors and records the breath rate continuously. This paper proposes a breath rate monitoring system called COVID-Beat, a wireless, low-cost, and contactless Wi-Fi-based continuous breath monitoring system. This sensor is developed using off-the-shelf commonly available embedded Internet of Thing device ESP32, and the performance is validated by conducting extensive experimentation. The breath rate is estimated by extracting the channel state information of the subcarriers. The system estimates the breath rate with a maximum accuracy of 99% and a minimum accuracy of 91%, achieved by advanced subcarrier selection and fusion method. The experimental results show superior performance over the existing breath rate monitoring technologies.

Wi-CAS: A Contactless Method for Continuous Indoor Human Activity Sensing Using Wi-Fi Devices.

With the new coronavirus raging around the world, home isolation has become an effective way to interrupt the spread of the virus. Effective monitoring of people in home isolation has also become a pressing issue. However, the large number of isolated people and the privatized isolated spaces pose challenges for traditional sensing techniques. Ubiquitous Wi-Fi offers new ideas for sensing people indoors. Advantages such as low cost, wide deployment, and high privacy make indoor human activity sensing technology based on Wi-Fi signals increasingly used. Therefore, this paper proposes a contactless indoor person continuous activity sensing method based on Wi-Fi signal Wi-CAS. The method allows for the sensing of continuous movements of home isolated persons. Wi-CAS designs an ensemble classification method based on Hierarchical Clustering (HEC) for the classification of different actions, which effectively improves the action classification accuracy while reducing the processing time. We have conducted extensive experimental evaluations in real home environments. By recording the activities of different people throughout the day, Wi-CAS is very sensitive to unusual activities of people and also has a combined activity recognition rate of 94.3%. The experimental results show that our proposed method provides a low-cost and highly robust solution for supervising the activities of home isolates.

Improving Machine Learning Classification Accuracy for Breathing Abnormalities by Enhancing Dataset.

The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus disease (COVID)-19, has appeared as a global pandemic with a high mortality rate. The main complication of COVID-19 is rapid respirational deterioration, which may cause life-threatening pneumonia conditions. Global healthcare systems are currently facing a scarcity of resources to assist critical patients simultaneously. Indeed, non-critical patients are mostly advised to self-isolate or quarantine themselves at home. However, there are limited healthcare services available during self-isolation at home. According to research, nearly 20-30% of COVID patients require hospitalization, while almost 5-12% of patients may require intensive care due to severe health conditions. This pandemic requires global healthcare systems that are intelligent, secure, and reliable. Tremendous efforts have been made already to develop non-contact sensing technologies for the diagnosis of COVID-19. The most significant early indication of COVID-19 is rapid and abnormal breathing. In this research work, RF-based technology is used to collect real-time breathing abnormalities data. Subsequently, based on this data, a large dataset of simulated breathing abnormalities is generated using the curve fitting technique for developing a machine learning (ML) classification model. The advantages of generating simulated breathing abnormalities data are two-fold; it will help counter the daunting and time-consuming task of real-time data collection and improve the ML model accuracy. Several ML algorithms are exploited to classify eight breathing abnormalities: eupnea, bradypnea, tachypnea, Biot, sighing, Kussmaul, Cheyne-Stokes, and central sleep apnea (CSA). The performance of ML algorithms is evaluated based on accuracy, prediction speed, and training time for real-time breathing data and simulated breathing data. The results show that the proposed platform for real-time data classifies breathing patterns with a maximum accuracy of 97.5%, whereas by introducing simulated breathing data, the accuracy increases up to 99.3%. This work has a notable medical impact, as the introduced method mitigates the challenge of data collection to build a realistic model of a large dataset during the pandemic.

Interim recommendations for the management of forensic investigation during the COVID-19 pandemic: An Italian perspective.

The Coronavirus disease 2019 (COVID-19) is spreading around the world, representing a global pandemic. In this context, governments from around the world suspended almost all education, industry and business activities, alongside restricting the movement of people. Nevertheless, during this period, the activity of the law enforcement and forensic investigators never stopped. At present, guidelines regarding forensic autopsies of SARS-CoV-2 virus-positive cases and the handling of potentially infected biological samples are available in literature. However, less attention has been given to the development of specific adjustments to the existing crime scene investigation protocols and procedures for this exceptional time. This manuscript aims to share the methods and strategies adopted for the investigation of high priority criminal cases during the pandemic. Furthermore, other pandemic-related processes are critically explored, in order to propose adjustments for any forensic services to be prepared to face similar challenges in the future. The overall goal of this manuscript is to provide a summary of the main measures and the procedures developed to make the operations possible, while safeguarding the technicians in the field and the activity in the forensic laboratory. In order to minimize the risk of infection for personnel, adjustments to the standard practice have been proposed for each of the different phases of crime scene management, i.e. CSI call policy, equipment preparation, working groups, procedure at the scene, chain of custody and analyses of the evidence at the forensic lab. As this is a current study, based on limited cases and limited sources in the literature, changes and updates to the indications provided in this paper may be needed in the near future, according to new virological data epidemiological trends.

Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron.

Contactless or non-invasive technology has a significant impact on healthcare applications such as the prediction of COVID-19 symptoms. Non-invasive methods are essential especially during the COVID-19 pandemic as they minimise the burden on healthcare personnel. One notable symptom of COVID-19 infection is a rapid respiratory rate, which requires constant real-time monitoring of respiratory patterns. In this paper, Software Defined Radio (SDR) based Radio-Frequency sensing technique and supervised machine learning algorithm is employed to provide a platform for detecting and monitoring various respiratory: eupnea, biot, bradypnea, sighing, tachypnea, and kussmaul. The variations in Channel State Information produced by human respiratory were utilised to identify distinct respiratory patterns using fine-grained Orthogonal Frequency-Division Multiplexing signals. The proposed platform based on the SDR and the Deep Multilayer Perceptron classifier exhibits the ability to effectively detect and classify the afore-mentioned distinct respiratory with an accuracy of up to 99%. Moreover, the effectiveness of the proposed scheme in terms of diagnosis accuracy, precision, recall, F1-score, and confusion matrix is demonstrated by comparison with a state-of-the-art machine learning classifier: Random Forest.

RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices.

Non-contact detection of the breathing patterns in a remote and unobtrusive manner has significant value to healthcare applications and disease diagnosis, such as in COVID-19 infection prediction. During the epidemic prevention and control period of COVID-19, non-contact approaches have great significance because they minimize the physical burden on the patient and have the least requirement of active cooperation of the infected individual. During the pandemic, these non-contact approaches also reduce environmental constraints and remove the need for extra preparations. According to the latest medical research, the breathing pattern of a person infected with COVID-19 is unlike the breathing associated with flu and the common cold. One noteworthy symptom that occurs in COVID-19 is an abnormal breathing rate; individuals infected with COVID-19 have more rapid breathing. This requires continuous real-time detection of breathing patterns, which can be helpful in the prediction, diagnosis, and screening for people infected with COVID-19. In this research work, software-defined radio (SDR)-based radio frequency (RF) sensing techniques and machine learning (ML) algorithms are exploited to develop a platform for the detection and classification of different abnormal breathing patterns. ML algorithms are used for classification purposes, and their performance is evaluated on the basis of accuracy, prediction speed, and training time. The results show that this platform can detect and classify breathing patterns with a maximum accuracy of 99.4% through a complex tree algorithm. This research has a significant clinical impact because this platform can also be deployed for practical use in pandemic and non-pandemic situations.

A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19.

The rapid spread of the novel coronavirus disease, COVID-19, and its resulting situation has garnered much effort to contain the virus through scientific research. The tragedy has not yet fully run its course, but it is already clear that the crisis is thoroughly global, and science is at the forefront in the fight against the virus. This includes medical professionals trying to cure the sick at risk to their own health; public health management tracking the virus and guardedly calling on such measures as social distancing to curb its spread; and researchers now engaged in the development of diagnostics, monitoring methods, treatments and vaccines. Recent advances in non-contact sensing to improve health care is the motivation of this study in order to contribute to the containment of the COVID-19 outbreak. The objective of this study is to articulate an innovative solution for early diagnosis of COVID-19 symptoms such as abnormal breathing rate, coughing and other vital health problems. To obtain an effective and feasible solution from existing platforms, this study identifies the existing methods used for human activity and health monitoring in a non-contact manner. This systematic review presents the data collection technology, data preprocessing, data preparation, features extraction, classification algorithms and performance achieved by the various non-contact sensing platforms. This study proposes a non-contact sensing platform for the early diagnosis of COVID-19 symptoms and monitoring of the human activities and health during the isolation or quarantine period. Finally, we highlight challenges in developing non-contact sensing platforms to effectively control the COVID-19 situation.