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An Efficient and Trusted Deep Learning Framework for Real-Time PPE Detection in Secure IoMT Environment

Verma, Anusha
http://rave.ohiolink.edu/etdc/view?acc_num=wright1736790477910174

Abstract Details

2024, Master of Science (MS), Wright State University, Computer Science.
Occupationally-acquired infections impact thousands of healthcare workers (HCWs) in the U.S., with many cases preventable through proper use of personal protective equipment (PPE). This study seeks to develop a robust system to enhance PPE compliance and reduce infection risks among HCWs. The objectives of this thesis are twofold: (1) to create a hybrid machine learning model that combines object detection and keypoint detection to ensure correct donning and doffing of PPE, and (2) to design a real-time feedback system using LED indicators and a display interface to offer actionable guidance to HCWs during PPE usage. The goal is to optimize the ML model for accurate PPE detection and evaluate its performance in IoT and edge systems for real-time feedback, ensuring effective user interaction. This approach aims to promote safer healthcare environments by improving PPE compliance and minimizing exposure risks. The findings of this thesis demonstrate that the developed model is compact, secure, and capable of real-time performance, making it well-suited for IoMT frameworks.
Fathi Amsaad, Ph.D. (Advisor)
Hugh P. Salehi, Ph.D. (Committee Member)
Wen Zhang, Ph.D. (Committee Member)
76 p.
Computer Science; Medical Imaging
Internet of Thing; PPE; Object Detection; Yolo; Adversarial attack; MQTT protocol

Recommended Citations

Citations

  • Verma, A. (2024). An Efficient and Trusted Deep Learning Framework for Real-Time PPE Detection in Secure IoMT Environment [Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1736790477910174

    APA Style (7th edition)

  • Verma, Anusha. An Efficient and Trusted Deep Learning Framework for Real-Time PPE Detection in Secure IoMT Environment. 2024. Wright State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1736790477910174.

    MLA Style (8th edition)

  • Verma, Anusha. "An Efficient and Trusted Deep Learning Framework for Real-Time PPE Detection in Secure IoMT Environment." Master's thesis, Wright State University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=wright1736790477910174

    Chicago Manual of Style (17th edition)

wright1736790477910174
36
© 2024, some rights reserved.Creative Commons License An Efficient and Trusted Deep Learning Framework for Real-Time PPE Detection in Secure IoMT Environment by Anusha Verma is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Wright State University and OhioLINK.
Release 3.2.12