Abstract

The COVID-19 outbreak has triggered a global health and economic crisis, necessitating widespread testing to control viral spread amidst rising cases and fatalities. The recommended testing method, a combined naso- and oropharyngeal swab, poses risks and demands limited protective gear. In response to the COVID-19 pandemic, we developed and tested the first autonomous swab robot station for Naso- and Oropharyngeal Coronavirus Screening (SR-NOCS). A force-sensitive robot running under a Cartesian impedance controller is employed to drive the swab to the sampling area. This groundbreaking device underwent two clinical studies-one conducted during the initial pandemic lockdown in Europe (early 2021) and the other, more recently, in a public place after the pandemic had subsided earlier in the year 2023. In total, 52 patients suspected of COVID-19 infection were included in these clinical studies. The results revealed a complete positive correlation between autonomous and manual sampling. The test subjects exhibited a high acceptance rate, all expressing a willingness to undergo future tests with SR-NOCS. Based on our findings, such systems could enhance testing capabilities, potentially conducting up to 300 tests per robot per day with consistent precision. The tests can be carried out with minimal supervision, reducing infection risks and effectively safeguarding patients and healthcare workers.

Details

Title
Autonomous swab robot for naso- and oropharyngeal COVID-19 screening
Author
Haddadin, Simon 1 ; Wilhelm, Dirk 2 ; Wahrmann, Daniel 3 ; Tenebruso, Fabio 3 ; Sadeghian, Hamid 4 ; Naceri, Abdeldjallil 4 ; Haddadin, Sami 4 

 Franka Emika GmbH, Munich, Germany 
 Technical University Munich, School for Medicine and Health, Klinikum rechts der Isar, Department of Surgery, Munich, Germany (GRID:grid.6936.a) (ISNI:0000000123222966) 
 Franka Emika GmbH, Munich, Germany (GRID:grid.6936.a) 
 Technical University Munich, Chair of Robotics and Systems Intelligence, School of Computation, Information and Technologies, Munich Institute of Robotics and Machine Intelligence, Munich, Germany (GRID:grid.6936.a) (ISNI:0000 0001 2322 2966) 
Pages
142
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2909067341
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.