A review of mobile sensing in the COVID-19 era

Health data science (2022). DOI: 10.34133 / 2022/9830476 “width =” 800 “height =” 447 “/>

Mapping of literature in a two-dimensional taxonomy. Credit: Health data science (2022). DOI: 10.34133 / 2022/9830476

Mobile detection has shown its power in pervasive and effective monitoring of COVID-19 across different population scales and duration over time, according to a study published in Health data science.

Behind this work are researchers from the Sensing System for Health Lab led by Dr. Laura Barnes at the University of Virginia. They worked to promote health and wellness using mobile tracking and data analytics techniques.

Mobile tracking, a digital surveillance tool, leverages sensors embedded in mobile devices such as smartphones and wearables. As mobile sensing has become a promising way to monitor pandemic trajectories by collecting data on an individual, community and global scale, this paper investigated the study designs, expected health outcomes, and existing limitations of such a mobile-based human subject. work to guide future practice. As such, this document stands out in a panoply of articles on the use of mobile devices for the response to COVID-19.

“We looked at the objectives and designs of existing work, 2) the duration of detection and population coverage, 3) the results and limitations, to better classify and understand this topic,” says Zhiyuan Wang, Ph.D. student with Sensing Systems for Health Lab.

“Existing work has demonstrated the ability of mobile detection to not only 1) detect infectious status remotely, but also 2) longitudinally track disease progression for personalized medicine, 3) passively track exposures, and 4) observe extensively the influence of the pandemic on the health of the population “, shares Professor Laura Barnes, director of the laboratory.

However, technical and social limitations still exist, including challenges related to data availability and system adoption, clinical and application issues, and ethical and privacy issues. These limitations have hindered further action by computer scientists, clinicians and epidemiologists in leveraging mobile detection for human health.

Current or emerging technologies can provide a solution to these constraints. For example, advances in data analytics and machine learning methods can help improve data quality due to their ability to process scattered, heterogeneous and multimodal mobile tracking data streams. In addition, mobile sensing on an even larger scale, particularly in the clinical setting, could be accomplished by leveraging the next generation of sensors and sensing platforms.

Other stakeholders may also have an impact on how mobile sensing can deliver clinical and social benefits. Such efforts may include mitigating potential threats to privacy, equity and health inequalities; promote technological and health literacy in all communities; and make decisions based on trust and shared that adequately balance risks and rewards.

Barnes and his team want to see more jobs in which computer scientists, clinicians and epidemiologists design and implement the study in collaboration with social science and public policy experts to enable more effective, scalable and socially equal mobile health systems for infectious diseases.

The new basic model improves the accuracy for the interpretation of remotely sensed images

More information:
Zhiyuan Wang et al, Mobile Sensing in the COVID-19 Era: A Review, Health data science (2022). DOI: 10.34133 / 2022/9830476

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Citation: A review of mobile detection in the COVID-19 era (2022, September 23) retrieved on September 23, 2022 from https://medicalxpress.com/news/2022-09-mobile-covid-era.html

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