CRISIS: Cyber-Physical Social Distancing Based on Multi-Modal Data From Mobile Devices
Journal
IEEE Transactions on Mobile Computing
Journal Volume
22
Journal Issue
5
Date Issued
2023-05-01
Author(s)
Huang, Yunfeng
Abstract
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.
Subjects
Mobility analytics | proximity detection | sensor fusion | social inferences | wireless fingerprinting
SDGs
Type
journal article