COVID-19 AEROSOL SUCTION ROBOT TO ASSIST DENTIST SURGERY BASED ON MOUTH OPENNESS DETECTION USING DEEP LEARNING

Riyanto Sigit, Aditia Yuliyanto, Moch Rochmad, Imam Safari Azhar

Research output: Contribution to journalArticlepeer-review

Abstract

As of November 6, 2022, COVID-19 cases have increased in the Southeast Asia Region by approximately +28%. In dentistry, the COVID-19 infection can be transmitted quickly through aerosol particles. Extra-Oral Suction (EOS) is a mechanism for sucking an aerosol of the patient to counter the transmission of COVID-19; nevertheless the EOS suction nozzle is still manually driven. This allows aerosol particles to escape the mechanism when the patient turns his head or shifts the position of the head. In this case, Visual Servoing (VS) is required; In the realm of image processing, specifically in the context of mouth openness detection, we explore the integration of autonomous control mechanisms to dynamically adjust the orientation of a nozzle. We used a PBVS (Position-Based Visual Servoing) approach integrated with the TensorFlow deep learning models, namely EfficientDet D0, Single Shot Detector (SSD) MobileNet, and SSD ResNet50. Based on the test results of mouth openness detection, EfficientDet has the highest accuracy reaching 100%, SSD MobileNet has an accuracy of 97%, and SSD ResNet50 has an accuracy of 98%. In addition, the system can track the position of the human mouth with a response speed of 135 px/s and an accuracy of 73.1%.

Original languageEnglish
Pages (from-to)1377-1391
Number of pages15
JournalInternational Journal of Innovative Computing, Information and Control
Volume19
Issue number5
DOIs
Publication statusPublished - Oct 2023

Keywords

  • COVID-19
  • Deep learning
  • Dentist surgery
  • Mouth tracking
  • Visual servoing

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