Indoor location and collision feedback for a powered wheelchair system using machine learning

Nils Bausch, Peter Shilling, David Sanders, Malik Jamal Musa Haddad, Ogechukwu Mercy Okonor, Giles Tewkesbury

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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In 2015 a powered wheelchair system that can detect and avoid objects was enhanced with a Raspberry Pi to extend the number of inputs the system could use to infer information about its environment. Wheelchair users are not always able to use simple controls such as joystick to drive, they may have to control the wheelchair using tongue, head or feet. This can make it much more difficult to learn how to drive and therefore is important to know how a user is progressing. The research described in this paper employs machine learning to uses wireless access points and predict its location, and with prolonged use will learn routes between rooms and buildings. The system uses location and accelerometer data to present information about driving patterns and collisions behaviour, to inform the wheelchair user and carer of issues while driving the wheelchair.
Original languageEnglish
Title of host publicationIntelliSys 2019 Intelligent Systems and Applications
Subtitle of host publicationProceedings of the 2019 Intelligent Systems Conference
EditorsYaxin Bi, Rahul Bhatia, Supriya Kapoor
Number of pages19
ISBN (Electronic)978-3-030-29516-5
ISBN (Print)978-3-030-29515-8
Publication statusPublished - 24 Aug 2019
EventIEEE SAI Intelligent Systems Conference 2019 - London, United Kingdom
Duration: 5 Sept 20196 Sept 2019

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Electronic)2194-5357


ConferenceIEEE SAI Intelligent Systems Conference 2019
Abbreviated titleIntelliSys 2019
Country/TerritoryUnited Kingdom
Internet address


  • indoor location
  • powered wheelchair
  • IMU
  • collision detection
  • interface
  • machine learning


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