Virtual Compass: Relative Positioning to Sense Mobile Social Interactions

A smartphone-based zero-effort method for mitigating epidemic propagation

A large number of epidemics, including COVID-19 and SARS, quickly swept the world and claimed the precious lives of large numbers of people. Due to the concealment and rapid spread of the virus, it is difficult to track down individuals with mild or asymptomatic symptoms with limited human resources. Building a low-cost and real-time epidemic early warning system to identify individuals who have been in contact with infected individuals and determine whether they need to be quarantined is an effective means to mitigate the spread of the epidemic. In this paper, we propose a smartphone-based zero-effort epidemic warning method for mitigating epidemic propagation. Firstly, we recognize epidemic-related voice activity relevant to epidemics spread by hierarchical attention mechanism and temporal convolutional network. Subsequently, we estimate the social distance between users through sensors built-in smartphone. Furthermore, we combine Wi-Fi network logs and social distance to comprehensively judge whether there is spatiotemporal contact between users and determine the duration of contact. Finally, we estimate infection risk based on epidemic-related vocal activity, social distance, and contact time. We conduct a large number of well-designed experiments in typical scenarios to fully verify the proposed method. The proposed method does not rely on any additional infrastructure and historical training data, which is conducive to integration with epidemic prevention and control systems and large-scale applications.

User-Centric Proximity Estimation Using Smartphone Radio Fingerprinting

The integration of infectious disease modeling with the data collection process is crucial to reach its maximum potential, and remains a significant research challenge. Ensuring a solid empirical foundation for models used to fill gaps in data and knowledge is of paramount importance. Personal wireless devices, such as smartphones, smartwatches and wireless bracelets, can serve as a means of bridging the gap between empirical data and the mathematical modeling of human contacts and networking. In this paper, we develop, implement, and evaluate concepts and architectures for advanced user-centric proximity estimation based on smartphone radio environment monitoring. We investigate innovative methods for the estimation of proximity, based on a person-radio-environment trace recorded by the smartphone, and define the proximity parameter. For this purpose, we developed a smartphone application and back-end services. The results show that, with the proposed procedure, we can estimate the proximity of two devices in terms of near, medium, and far distance with reasonable accuracy in real-world case scenarios.

Machine Learning Estimation of COVID-19 Social Distance using Smartphone Sensor Data

Airborne infectious diseases such as COVID-19 spread when healthy people are in close proximity to infected people. Technology-assisted methods to detect proximity in order to alert people are needed. In this work we systematically investigating Machine Learning (ML) methods to detect proximity by analyzing data gathered from smartphones' built-in Bluetooth, accelerometer and gyroscope sensors. We extracted 20 statistical features from raw sensor data, which were then classified (< 6ft or not) and regressed (distance estimate) using ML algorithms. We found that elliptical filtering of accelerometer and gyroscope sensors signal improved the performance of ML regression. The most predictive features were z-axis mean and fourth momentum for the accelerometer sensors, z-axis mean y-axis mean for the gyroscope sensor, and advertiser time and mean RSSI for Bluetooth radio. After rigorous evaluation of the performance of 19 ML classification and regression methods, we found that ensemble (boosted and bagged tree) methods and regression trees ML algorithms performed best when using data from a combination of Bluetooth radio, accelerometer and the gyroscope. We were able to classify proximity (< 6ft or not) with 100% accuracy using the accelerometer sensor and with 62%-97% accuracy with the Bluetooth radio.

A Unified Approach to Uncertainty-Aware Ubiquitous Localisation of Mobile Users

Localisation is a standard feature in many mobile applications today, and there are numerous techniques for determining a user’s location both indoors and outdoors. The provided location information is often organised in a format tailored to a particular localisation system’s needs and restrictions, making the use of several systems in one application cumbersome. The presented approach models the details of localisation systems and uses this model to create a unified view on localisation in which special attention is paid to uncertainty coming from different localisation conditions and to its presentation to the user. The work discusses technical considerations, challenges and issues of the approach, and reports on a user study on the acceptance of a mobile application’s behaviour reflecting the approach. The results of the study show the suitability of the approach and reveal users’ preference toward automatic and informed changes they experienced while using the application.