DISTERNING: Distance Estimation Using Machine Learning Approach for COVID-19 Contact Tracing and Beyond
نویسندگان
چکیده
Since the coronavirus disease 19 (COVID-19) outbreak, epidemiological analysis has raised a strong requirement for more effective and accurate contact tracing solution. However, existing solutions either lacked evaluation of proximity or features used were susceptible to certain negative environmental factors (e.g., body shielding). In this article, we propose novel distance estimation algorithm based on machine learning tracing: DISTERNING, where leverage algorithms including Learning Vector Quantization, Regression, Deep Feed-forward (DFF) Neural Network, data processing methods, digital filters process Bluetooth signal information collected by mobile phone estimation. A scheme edge computing is also proposed deployment due requirements computational power. Compared with solutions, our considers that have significant influence in reality. The results show when influenced real-world factors, employing DISTERNING can keep accuracy estimated reliable. output be combined some medical models conduct infection risk assessments.
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ژورنال
عنوان ژورنال: IEEE Journal on Selected Areas in Communications
سال: 2022
ISSN: ['0733-8716', '1558-0008']
DOI: https://doi.org/10.1109/jsac.2022.3214277