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Citation link: https://media.suub.uni-bremen.de/handle/elib/6677

Publisher DOI: https://doi.org/10.1007/978-3-030-85616-8_25
 
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Strategically Using Applied Machine Learning for Accessibility Documentation in the Built Environment


Authors: Lange, Marvin 
Kirkham, Reuben  
Tannert, Benjamin  
Abstract: 
There has been a considerable amount of research aimed at automating the documentation of accessibility in the built environment. Yet so far, there has been no fully automatic system that has been shown to reliably document surface quality barriers in the built environment in real-time. This is a mixed problem of HCI and applied machine learning, requiring the careful use of applied machine learning to address the real-world concern of practical documentation. To address this challenge, we offer a framework for designing applied machine learning approaches aimed at documenting the (in)accessibility of the built environment. This framework is designed to take into account the real-world picture, recognizing that the design of any accessibility documentation system has to take into account a range of factors that are not usually considered in machine learning research. We then apply this framework in a case study, illustrating an approach which can obtain a f-ratio of 0.952 in the best-case scenario.
Keywords: Accessibility; Built-Environment; Documentation
Issue Date: 2021
Publisher: Springer
Journal/Edited collection: Human-Computer Interaction – INTERACT 2021 
Series: Lecture Notes in Computer Science 
Start page: 426
End page: 448
Band: 2
Type: Artikel/Aufsatz
ISBN: 978-3-030-85615-1
ISSN: 978-3-030-85616-8
Institution: Hochschule Bremen 
Faculty: Hochschule Bremen - Fakultät 4: Elektrotechnik und Informatik 
Appears in Collections:Bibliographie HS Bremen

  

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