Skip to content

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

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

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
PublisherSpringer
Pages721-739
Number of pages19
Volume1
ISBN (Electronic)978-3-030-29516-5
ISBN (Print)978-3-030-29515-8
DOIs
Publication statusPublished - 24 Aug 2019
EventIEEE SAI Intelligent Systems Conference 2019 - London, United Kingdom
Duration: 5 Sep 20196 Sep 2019
https://saiconference.com/IntelliSys
https://saiconference.com/Conferences/IntelliSys2019

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer
Volume1037
ISSN (Electronic)2194-5357

Conference

ConferenceIEEE SAI Intelligent Systems Conference 2019
Abbreviated titleIntelliSys 2019
CountryUnited Kingdom
CityLondon
Period5/09/196/09/19
Internet address

Documents

  • Indoor Location and Collision Feedback for a Powered Wheelchair System using Machine Learning

    Rights statement: This is a post-peer-review, pre-copyedit version of an article published in Intelligent Systems and Applications. IntelliSys 2019 Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Systems Conference. Advances in Intelligent Systems and Computing, vol 1037. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-29516-5_54.

    Accepted author manuscript (Post-print), 4.52 MB, PDF document

    Due to publisher’s copyright restrictions, this document is not freely available to download from this website until: 24/08/20

Related information

Relations Get citation (various referencing formats)

ID: 11962924