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  4. Strategically Using Applied Machine Learning for Accessibility Documentation in the Built Environment
 
Verlagslink DOI
10.1007/978-3-030-85616-8_25

Strategically Using Applied Machine Learning for Accessibility Documentation in the Built Environment

Veröffentlichungsdatum
2021
Autoren
Lange, Marvin  
Kirkham, Reuben  
Tannert, Benjamin  
Zusammenfassung
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.
Schlagwörter
Accessibility

; 

Built-Environment

; 

Documentation
Verlag
Springer
Institution
Hochschule Bremen  
Fachbereich
Hochschule Bremen - Fakultät 4: Elektrotechnik und Informatik  
Dokumenttyp
Artikel/Aufsatz
Zeitschrift/Sammelwerk
Human-Computer Interaction – INTERACT 2021  
Serie(s)
Lecture Notes in Computer Science  
Band
2
Startseite
426
Endseite
448
Sprache
Englisch

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