Evidence map›Paper›PMID 34851982›Full record

ArticlePloS one2021

COVID surveillance robot: Monitoring social distancing constraints in indoor scenarios.

Adarsh Jagan Sathyamoorthy, Utsav Patel, Moumita Paul, Yash Savle, Dinesh Manocha

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

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  18. Robots Under COVID-19 Pandemic: A Comprehensive Survey.IEEE access : practical innovations, open solutions · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Adarsh Jagan SathyamoorthyDepartment of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America.ORCID 0000-0002-9678-4748
Utsav PatelDepartment of Computer Science, University of Maryland, College Park, Maryland, United States of America.
Moumita PaulInstitute of Systems Research, University of Maryland, College Park, Maryland, United States of America.
Yash SavleInstitute of Systems Research, University of Maryland, College Park, Maryland, United States of America.
Dinesh ManochaDepartment of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Observing social/physical distancing norms between humans has become an indispensable precaution to slow down the transmission of COVID-19. We present a novel method to automatically detect pairs of humans in a crowded scenario who are not maintaining social distancing, i.e. about 2 meters of space between them using an autonomous mobile robot and existing CCTV (Closed-Circuit TeleVision) cameras. The robot is equipped with commodity sensors, namely an RGB-D (Red Green Blue-Depth) camera and a 2-D lidar to detect social distancing breaches within their sensing range and navigate towards the location of the breach. Moreover, it discreetly alerts the relevant people to move apart by using a mounted display. In addition, we also equip the robot with a thermal camera that transmits thermal images to security/healthcare personnel who monitors COVID symptoms such as a fever. In indoor scenarios, we integrate the mobile robot setup with a static wall-mounted CCTV camera to further improve the number of social distancing breaches detected, accurately pursuing walking groups of people etc. We highlight the performance benefits of our robot + CCTV approach in different static and dynamic indoor scenarios.

Indexed as

Physical DistancingRoboticsAlgorithmsCOVID-19Environmental MonitoringHumansPhotographySARS-CoV-2

Identifiers

PMID34851982
PMCPMC8635356

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.