BLE Based Exposure Notification System for Contact Tracing

Satyam, Deepak; Maheswari, B. Uma

doi:10.1109/i-smac52330.2021.9640971

Cited by 1 publication

(2 citation statements)

References 11 publications

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“…Recognizing the importance of contact tracing, many studies on contact tracing systems have been developed [32]. Some commercial solutions conduct contact tracing with GPS [33], RFID [34], ultra-wideband [35], BLE [36][37][38], Wi-Fi [31,39], cellular [40][41][42], vision [43], and other technologies. To sense mobile social interactions, Banerjee et al [44] proposed virtual compass, which effectively perceives the mutual distance between users, but cannot obtain direction information.…”

Section: Contact Tracingmentioning

confidence: 99%

“…The precise pedestrian position from the foot-mounted INS module is sent to the smartphone via Bluetooth low energy (Ble) and synchronizes with the measurements of smartphone-embedded MEMS sensors. We use the final position error over total traveled distance (ε/TTD), step detection rate (SDR), (37) step length error (SLE), and circular error probability (CEP) as metrics to quantify the performance of the proposed positioning method.…”

Section: Performance Measures and Experimental Setupmentioning

confidence: 99%

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

Wang

et al. 2023

EURASIP J. Adv. Signal Process.

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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.

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