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Predicting Fit of Filtering Facepiece Respirators Through New Face Anthropometry and 3D Face Shape Acquisition
Predicting Fit of Filtering Facepiece Respirators Through New Face Anthropometry and 3D Face Shape Acquisition
Detailed Information
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151520
- ISBN
- 9798383163283
- DDC
- 741
- 저자명
- Yu, Minji.
- 서명/저자
- Predicting Fit of Filtering Facepiece Respirators Through New Face Anthropometry and 3D Face Shape Acquisition
- 발행사항
- [Sl] : University of Minnesota, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 148 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Griffin, Linsey.
- 학위논문주기
- Thesis (Ph.D.)--University of Minnesota, 2024.
- 초록/해제
- 요약This research investigated the relationship between face shape and respirator fit, with a focus on enhancing the fit and design of filtering facepiece respirators (FFRs). The study addressed the need for improved respirator fit, particularly in occupational settings where respiratory protection is paramount for safeguarding workers' health.Combining anthropometric analysis, three-dimensional (3D) scanning technology, quantitative fit testing, and predictive modeling, this research assessed the impact of face shape on respirator fit. It examined the limitations of traditional two-dimensional anthropometric measures in predicting FFR fit and proposed a novel framework based on 3D-derived face shapes and dimensions.Key findings highlighted the importance of predicting respirator fit based on diverse facial shapes and sizes. By integrating face anthropometric and geometric data into respirator design processes, manufacturers can develop more ergonomic and effective respiratory protective equipment. Such predictive capabilities can aid individuals in selecting respirators that are more likely to provide a secure fit, thereby enhancing the overall effectiveness of protection and reducing the risk of exposure to airborne hazards.The implications of this research extend beyond occupational safety and health, encompassing broader public health considerations, particularly in the context of infectious disease outbreaks like the COVID-19 pandemic. By advancing the understanding of respirator fit, this research contributes to the development of evidence-based practices for respiratory protection, ultimately enhancing the well-being and safety of workers worldwide.
- 일반주제명
- Design
- 일반주제명
- Biomedical engineering
- 일반주제명
- Computer science
- 키워드
- 3D scanning
- 키워드
- Face shape
- 키워드
- Fit prediction
- 키워드
- Respirators
- 기타저자
- University of Minnesota Design
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798383163283
■035 ▼a(MiAaPQ)AAI31301480
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a741
■1001 ▼aYu, Minji.
■24510▼aPredicting Fit of Filtering Facepiece Respirators Through New Face Anthropometry and 3D Face Shape Acquisition
■260 ▼a[Sl]▼bUniversity of Minnesota▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a148 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Griffin, Linsey.
■5021 ▼aThesis (Ph.D.)--University of Minnesota, 2024.
■520 ▼aThis research investigated the relationship between face shape and respirator fit, with a focus on enhancing the fit and design of filtering facepiece respirators (FFRs). The study addressed the need for improved respirator fit, particularly in occupational settings where respiratory protection is paramount for safeguarding workers' health.Combining anthropometric analysis, three-dimensional (3D) scanning technology, quantitative fit testing, and predictive modeling, this research assessed the impact of face shape on respirator fit. It examined the limitations of traditional two-dimensional anthropometric measures in predicting FFR fit and proposed a novel framework based on 3D-derived face shapes and dimensions.Key findings highlighted the importance of predicting respirator fit based on diverse facial shapes and sizes. By integrating face anthropometric and geometric data into respirator design processes, manufacturers can develop more ergonomic and effective respiratory protective equipment. Such predictive capabilities can aid individuals in selecting respirators that are more likely to provide a secure fit, thereby enhancing the overall effectiveness of protection and reducing the risk of exposure to airborne hazards.The implications of this research extend beyond occupational safety and health, encompassing broader public health considerations, particularly in the context of infectious disease outbreaks like the COVID-19 pandemic. By advancing the understanding of respirator fit, this research contributes to the development of evidence-based practices for respiratory protection, ultimately enhancing the well-being and safety of workers worldwide.
■590 ▼aSchool code: 0130.
■650 4▼aDesign
■650 4▼aBiomedical engineering
■650 4▼aComputer science
■653 ▼a3D scanning
■653 ▼aFace anthropometry
■653 ▼aFace shape
■653 ▼aFit prediction
■653 ▼aRespirators
■690 ▼a0389
■690 ▼a0984
■690 ▼a0541
■690 ▼a0354
■71020▼aUniversity of Minnesota▼bDesign.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0130
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162074▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
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