CRISIS: Cyber-Physical Social Distancing Based on Multi-Modal Data From Mobile Devices

Multi-modal sensors on mobile devices (e.g., smart watches and smartphones) have been widely used to ubiquitously perceive human mobility and body motions for understanding social interactions between people. This work investigates the correlations between the multi-modal data observed by mobile devices and social closeness among people along their trajectories. To close the gap between cyber-world data distances and physical-world social closeness, this work quantifies the cyber distances between multi-modal data. The human mobility traces and body motions are modeled as cyber signatures based on ambient Wi-Fi access points and accelerometer data observed by mobile devices that explicitly indicate the mobility similarity and movement similarity between people. To verify the merits of modeled cyber distances, we design the localization-free CybeR-physIcal Social dIStancing (CRISIS) system that detects if two persons are physically non-separate (i.e., not social distancing) due to close social interactions (e.g., taking similar mobility traces simultaneously or having a handshake with physical contact). Extensive experiments are conducted in two small-scale environments and a large-scale environment with different densities of Wi-Fi networks and diverse mobility and movement scenarios. The experimental results indicate that our approach is not affected by uncertain environmental conditions and human mobility with an overall detection accuracy of 98.41% in complex mobility scenarios. Furthermore, extensive statistical analysis based on 2-dimensional (2D) and 3-dimensional (3D) mobility datasets indicates that the proposed cyber distances are robust and well-synchronized with physical proximity levels. © 2002-2012 IEEE.

Contact Tracing Platform in OSN for Prevention of Infectious Disease Outbreaks

To limit the spread of COVID-19, social distancing measurements and contact tracing have become popular strategies implemented worldwide. In addition to manual contact tracing, smartphone-based applications based on proximity detection have emerged to speed up the discovery of potential infectious individuals. However, so far, their effectiveness has been limited, mainly due to privacy issues. A new tracing mechanism is represented by Online Social Networks (OSNs), which provide a successful way to track, share and exchange information in real-time. Being extremely popular and largely used by citizens, OSNs are less exposed to privacy concerns. In this paper, we present an OSN-based contact tracing platform called TraceMe to reduce the spread of the epidemic. The proposal currently targets COVID-19, but it can be used in presence of other infectious diseases, like Ebola, swine flue, etc. TraceMe implements conventional contact tracing based on physical proximity and, in addition, it leverages OSNs to identify other contacts potentially exposed to the virus. To efficiently find the targeted social community, while saving the time complexity, a clique-based method is applied. Performance evaluation based on a realistic dataset shows that TraceMe is able to analyse large-scale social networks in order to find, and then alert, the tight communities of contacts that are at high risk of infection. © 2023 IEEE.

Impact of Using Soft Exposure Thresholds in Automatic Contact Tracing

Current automatic exposure notification apps primarily operate based on hard distance/time threshold guidelines (e.g., 2 m/15 min in the United States) to determine exposures due to close contacts. However, the possibility of virus transmission through inhalation for distances over the specified distance threshold might necessitate consideration of soft distance/time thresholds to accommodate all transmission scenarios. In this paper, using a simplifying approximation on the instantaneous rate of the viral exposure versus distance, we extend the definition of "contact"by proposing a soft distance/time threshold which includes the possibility of getting exposed at any distance (within certain limits) around an infected person. We then analyze the performance of automatic exposure notification with Bluetooth-based proximity detection by comparing the exposure results when soft or hard thresholds are used. This study is done through an agent-based simulation platform that allows for a comprehensive analysis using several system parameters. By tuning the parameters of the proposed soft thresholds, a more accurate determination of possible exposures at any distance would be possible. This would enhance the effectiveness of an automatic contact tracing system. Our results indicate the noticeable impact of using the soft distance/time threshold on the exposure detection accuracy. © 2022 IEEE.

Proximity Detection During Epidemics: Direct UWB TOA Versus Machine Learning Based RSSI.

In this paper, we compare the direct TOA-based UWB technology with the RSSI-based BLE technology using machine learning algorithms for proximity detection during epidemics in terms of complexity of implementation, availability in existing smart phones, and precision of the results. We establish the theoretical limits on the precision and confidence of proximity estimation for both technologies using the Cramer Rao Lower Bound (CRLB) and validate the theoretical foundations using empirical data gathered in diverse practical operating scenarios. We perform our empirical experiments at eight distances in three flat environments and one non-flat environment encompassing both Line of Sight (LOS) and Obstructed-LOS (OLOS) situations. We also analyze the effects of various postures (eight angles) of the person carrying the sensor, and four on-body locations of the sensor. To estimate the range with BLE RSSI, we use 14 features for training the Gradient Boosted Machines (GBM) learning algorithm and we compare the precision of results with those obtained from memoryless UWB TOA ranging algorithm. We show that the memoryless UWB TOA algorithm achieves 93.60% confidence, slightly outperforming the 92.85% confidence of the BLE RSSI with more complex GBM machine learning (ML) algorithm and the need for substantial training. The training process for the RSSI-based BLE social distance measurements involved 3000 measurements to create a training dataset for each scenario and post-processing of data to extract 14 features of RSSI, and the ML classification algorithm consumed 200 s of computational time. The memoryless UWB ranging algorithm achieves more robust results without any need for training in less than 0.5 s of computation time.

On the Performance of Automatic Exposure Determination Using Bluetooth-based Proximity Estimation

The proximity detection mechanism in current automatic exposure notification systems is typically based on the Bluetooth signal strength from the individual's mobile phone. However, there is an underlying error in this proximity detection methodology that could result in wrong exposure decisions i.e., false negatives and false positives. A false negative error happens if a truly exposed individual is mistakenly identified as not exposed. This misidentification could result in further spread of the virus by the exposed (yet undetected) individual. Likewise, when a non-exposed individual is incorrectly identified as exposed, a false positive error occurs. This could lead to unnecessary quarantine of the individual;and therefore, incurring further economic cost. In this paper, using a simulation platform and a notion of proximity detection error, we investigate the performance of the system in terms of false exposure determinations. Knowledge of how the Bluetooth-based proximity detection error impacts such false determinations and identification of methodologies that can reduce this impact will be helpful to enhance the effectiveness of an automatic contact tracing system. Our preliminary results indicate the substantial impact of the proximity estimation error on the exposure detection accuracy. The results also suggest how proper filtering of distance measurements may reduce this impact. © 2022 IEEE.

Self-localization and interpersonal proximity detection for public transit accessibility and safety

Public transit stations and hubs are difficult to navigate for people with visual impairments. Moreover, public transit has been affected disproportionately by the social distancing requirements consequent to the COVID-19 pandemic. It is the objective of this dissertation to provide a technology for addressing these concerns in the frame of a mobile app named RouteMe2. The technology provides micro- routing and guidance to visually impaired travelers through complex routes in transit hubs. This work also includes the study to monitor the distance between the travelers inside the bus for social distancing application. Reducing the risk of air-born viral infections by social distancing can contribute to improving the overall safety of the public transit.The key enablers of this technology are sufficiently accurate self-localization and micro-routing as well as effective communication of the contextual spatiotemporal information with the visually impaired users. The accuracy of the self- localization in the outdoor environments is challenged by poor Global Positioning System (GPS) reception due to tall nearby buildings that may obscure view of one or more satellites - a.k.a shading. Shading is very common in urban environments, and is a major cause of GPS failure. In order to mitigate the effect of shading, I statistically fuse the signals received from GPS as well as a small number of Bluetooth Low Energy (BLE) beacons. I further pair the statistical fusion with a Bayes discrete filter tracker to increase the self-localization accuracy. Experiments were conducted at San Jose Diridon light rail station to quantitatively assess the performance of the resulting system.I have designed and implemented certain features and functionalities of RouteMe2 to provide effective communication of the in-context spatio-temporal information with visually impaired users while they use the app. I leveraged our previously published focus group study conducted with visually impaired people as well as reviewing the user interface of the existing related apps to improve the user experience of RouteMe2 the detail of which is presented.I further assess the ability of two RSSI-based methods at detecting interpersonal distances shorter than 1 or 2 meters. One method uses the power received from the smartphone carried by another person. The other method measures the disparity in the power received by the two smartphones from one or more fixed BLE beacons. The results show that use of the RSSI disparity enables discrimination measures that are as good or better than using the RSSI received from another smartphone. I demonstrate the potential of a system that uses BLE beacons, placed inside a vehicle, to localize a passenger within the length of the vehicle with an accuracy better than 1 meter. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Evaluation of the Bluetooth-based Proximity Estimation for Automatic Exposure Determination

A commonly used methodology to estimate the proximity of two individuals in an automatic exposure notification system uses the signal strength of the Bluetooth signal from their mobile phones. However, there is an underlying error in Bluetooth-based proximity detection that can result in wrong exposure decisions. A wrong decision in the exposure determination leads to two types of errors: false negatives and false positives. A false negative occurs when an exposed individual is incorrectly identified as not exposed. Similarly, a false positive occurs when a non-exposed individual is mistakenly identified as exposed. Both errors have costly implications and can ultimately determine the effectiveness of Bluetooth-based automatic exposure notification in containment of pandemics such as COVID-19. In this paper, we present a platform that allows for the analysis of the system performance under various parameters. This platform enables us to gain a better understanding on how the underlying technology error propagates through the contact tracing system. Preliminary results show the considerable impact of the Bluetooth-based proximity estimation error on false exposure determination. Alternatively, using this platform, analysis can be performed to determine the acceptable accuracy level of a proximity detection mechanism in order to have a more effective contact tracing solution. © 2022 IEEE.

Internet of Things Application in Controlling the Coronavirus Disease Spread in Hajj Season

Coronavirus Disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently a threat to the global human population. Infectious viruses, such as SARS-CoV-2, are easily transmitted from person to person and spread very quickly. These viruses are likely to spread anywhere there are massive crowds in confined spaces-and the Hajj pilgrimage to Makkah, Saudi Arabia is no exception. This work aims to prevent the spread of infection in the early stages of an outbreak. This paper explores the various methods for monitoring and controlling infectious disease during the Hajj including strengthening disease control and methods for providing the Saudi Ministry of Health (MOH) insights to enable them to plan for the specific challenges of controlling Coronavirus disease during the Hajj. This paper proposes a model, based on the Radio Frequency Identification Devices (RFID), Global Positioning System (GPS), wearable watch technology, and cloud computing infrastructure, which detects and monitors infected pilgrims and also aids in the identification of those pilgrims exposed to sources of a virus. Disciplinary: Information System, Technology, and Application, Healthcare Management. (C) 2022 INT TRANS J ENG MANAG SCI TECH.

Immediate Proximity Detection Using Wi-Fi-Enabled Smartphones

Smartphone apps for exposure notification and contact tracing have been shown to be effective in controlling the COVID-19 pandemic. However, Bluetooth Low Energy tokens similar to those broadcast by existing apps can still be picked up far away from the transmitting device. In this paper, we present a new class of methods for detecting whether or not two WiFi-enabled devices are in immediate physical proximity, i.e. 2 or fewer meters apart, as established by the U.S. Centers for Disease Control and Prevention (CDC). Our goal is to enhance the accuracy of smartphone-based exposure notification and contact tracing systems. We present a set of binary machine learning classifiers that take as input pairs of Wi-Fi RSSI fingerprints. We empirically verify that a single classifier cannot generalize well to a range of different environments with vastly different numbers of detectable Wi-Fi Access Points (APs). However, specialized classifiers, tailored to situations where the number of detectable APs falls within a certain range, are able to detect immediate physical proximity significantly more accurately. As such, we design three classifiers for situations with low, medium, and high numbers of detectable APs. These classifiers distinguish between pairs of RSSI fingerprints recorded 2 or fewer meters apart and pairs recorded further apart but still in Bluetooth range. We characterize their balanced accuracy for this task to be between 66.8% and 77.8%.

Passive Smartphone Contact Tracing and Continuous COVID-19 Infection Risk Assessment

COVID-19 has infected millions since November 2019. The virus spreads through close contact with those who are infected. People are often unaware of or lose track of their behaviors, which increase their risk of infection. Passive methods to continuously monitor and track dangerous user behaviors, maintain and update a COVID risk score can enable high risk users to take preventive measures early. At the organization or institution level, such systems can provide insights on organization-wide patterns that exacerbate disease spread. This paper presents our vision of pervasive, continuous infectious disease contact tracing, risky behavior tracking and continuous risk score calculation from contact, place visit information and reported behaviors. As a specific example, we describe the research, design and development of the android app Goatvid Trace that continuously gathers smartphone sensor data, using it to calculcate smartphone users' risk of exposure to COVID-19. Machine Learning methods for proximity detection from smart- phone Bluetooth RSSI signals are also described. GoatVid trace was deployed and evaluated on a small university community. Our evaluation study found that the mean COVID-19 risk score of college students in our study was 25.6%. Our risk score model correlated well with subject questionnaire responses with an R of 0.6166. The machine learning models for proximity detection estimated distances between two phones with a Cross Validation RMSE of 1.58766. © 2021 IEEE.

Perturbed-Location Mechanism for Increased User-Location Privacy in Proximity Detection and Digital Contact-Tracing Applications.

Future social networks will rely heavily on sensing data collected from users' mobile and wearable devices. A crucial component of such sensing will be the full or partial access to user's location data, in order to enable various location-based and proximity-detection-based services. A timely example of such applications is the digital contact tracing in the context of infectious-disease control and management. Other proximity-detection-based applications include social networking, finding nearby friends, optimized shopping, or finding fast a point-of-interest in a commuting hall. Location information can enable a myriad of new services, among which we have proximity-detection services. Addressing efficiently the location privacy threats remains a major challenge in proximity-detection architectures. In this paper, we propose a location-perturbation mechanism in multi-floor buildings which highly protects the user location, while preserving very good proximity-detection capabilities. The proposed mechanism relies on the assumption that the users have full control of their location information and are able to get some floor-map information when entering a building of interest from a remote service provider. In addition, we assume that the devices own the functionality to adjust to the desired level of accuracy at which the users disclose their location to the service provider. Detailed simulation-based results are provided, based on multi-floor building scenarios with hotspot regions, and the tradeoff between privacy and utility is thoroughly investigated.

Using Privacy Respecting Sound Analysis to Improve Bluetooth Based Proximity Detection for COVID-19 Exposure Tracing and Social Distancing.

We propose to use ambient sound as a privacy-aware source of information for COVID-19-related social distance monitoring and contact tracing. The aim is to complement currently dominant Bluetooth Low Energy Received Signal Strength Indicator (BLE RSSI) approaches. These often struggle with the complexity of Radio Frequency (RF) signal attenuation, which is strongly influenced by specific surrounding characteristics. This in turn renders the relationship between signal strength and the distance between transmitter and receiver highly non-deterministic. We analyze spatio-temporal variations in what we call "ambient sound fingerprints". We leverage the fact that ambient sound received by a mobile device is a superposition of sounds from sources at many different locations in the environment. Such a superposition is determined by the relative position of those sources with respect to the receiver. We present a method for using the above general idea to classify proximity between pairs of users based on Kullback-Leibler distance between sound intensity histograms. The method is based on intensity analysis only, and does not require the collection of any privacy sensitive signals. Further, we show how this information can be fused with BLE RSSI features using adaptive weighted voting. We also take into account that sound is not available in all windows. Our approach is evaluated in elaborate experiments in real-world settings. The results show that both Bluetooth and sound can be used to differentiate users within and out of critical distance (1.5 m) with high accuracies of 77% and 80% respectively. Their fusion, however, improves this to 86%, making evident the merit of augmenting BLE RSSI with sound. We conclude by discussing strengths and limitations of our approach and highlighting directions for future work.

Performance Evaluation of COVID-19 Proximity Detection Using Bluetooth LE Signal.

The risk of COVID-19 transmission increases when an uninfected person is less than 6 ft from an infected person for longer than 15 minutes. Infectious disease experts working on the COVID-19 pandemic call this high-risk situation being Too Close for Too Long (TCTL). Consequently, the problem of detecting the TCTL situation in order to maintain appropriate social distance has attracted considerable attention recently. One of the most prominent TCTL detection ideas being explored involves utilizing the Bluetooth Low-Energy (BLE) Received Signal Strength Indicator (RSSI) to determine whether the owners of two smartphones are observing the acceptable social distance of 6 ft. However, using RSSI measurements to detect the TCTL situation is extremely challenging due to the significant signal variance caused by multipath fading in indoor radio channel, carrying the smartphone in different pockets or positions, and differences in smartphone manufacturer and type of the device. In this study we utilize the Mitre Range Angle Structured (MRAS) Private Automated Contact Tracing (PACT) dataset to extensively evaluate the effectiveness of Machine Learning (ML) algorithms in comparison to classical estimation theory techniques to solve the TCTL problem. We provide a comparative performance evaluation of proximity classification accuracy and the corresponding confidence levels using classical estimation theory and a variety of ML algorithms. As the classical estimation method utilizes RSSI characteristics models, it is faster to compute, is more explainable, and drives an analytical solution for the precision bounds proximity estimation. The ML algorithms, Support Vector Machines (SVM), Random Forest, and Gradient Boosted Machines (GBM) utilized thirteen spatial, time-domain, frequency-domain, and statistical features extracted from the BLE RSSI data to generate the same results as classical estimation algorithms. We show that ML algorithms can achieve 3.60%~19.98% better precision, getting closer to achievable bounds for estimation.