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

doi:10.1007/978-3-030-29516-5_54

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

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

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 language	English
Title of host publication	IntelliSys 2019 Intelligent Systems and Applications
Subtitle of host publication	Proceedings of the 2019 Intelligent Systems Conference
Editors	Yaxin Bi, Rahul Bhatia, Supriya Kapoor
Publisher	Springer
Pages	721-739
Number of pages	19
Volume	1
ISBN (Electronic)	978-3-030-29516-5
ISBN (Print)	978-3-030-29515-8
DOIs	https://doi.org/10.1007/978-3-030-29516-5_54
Publication status	Published - 24 Aug 2019
Event	IEEE SAI Intelligent Systems Conference 2019 - London, United Kingdom Duration: 5 Sept 2019 → 6 Sept 2019 https://saiconference.com/IntelliSys https://saiconference.com/Conferences/IntelliSys2019

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	Springer
Volume	1037
ISSN (Electronic)	2194-5357

Conference

Conference	IEEE SAI Intelligent Systems Conference 2019
Abbreviated title	IntelliSys 2019
Country/Territory	United Kingdom
City	London
Period	5/09/19 → 6/09/19
Internet address	https://saiconference.com/IntelliSys https://saiconference.com/Conferences/IntelliSys2019

Keywords

indoor location
powered wheelchair
IMU
collision detection
interface
machine learning

Access to Document

10.1007/978-3-030-29516-5_54

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

Using artificial intelligence to share control of a powered-wheelchair between a wheelchair user and an intelligent sensor system
Sanders, D., Gegov, A. & Haddad, M.
Engineering and Physical Sciences Research Council
1/11/18 → 1/11/22
Project: Research

File

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Bausch, N., Shilling, P., Sanders, D., Haddad, M. J. M., Okonor, O. M., & Tewkesbury, G. (2019). Indoor location and collision feedback for a powered wheelchair system using machine learning. In Y. Bi, R. Bhatia, & S. Kapoor (Eds.), IntelliSys 2019 Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Systems Conference (Vol. 1, pp. 721-739). (Advances in Intelligent Systems and Computing; Vol. 1037). Springer. https://doi.org/10.1007/978-3-030-29516-5_54

Bausch, Nils ; Shilling, Peter ; Sanders, David et al. / Indoor location and collision feedback for a powered wheelchair system using machine learning. IntelliSys 2019 Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Systems Conference. editor / Yaxin Bi ; Rahul Bhatia ; Supriya Kapoor. Vol. 1 Springer, 2019. pp. 721-739 (Advances in Intelligent Systems and Computing).

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title = "Indoor location and collision feedback for a powered wheelchair system using machine learning",

abstract = "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.",

keywords = "indoor location, powered wheelchair, IMU, collision detection, interface, machine learning",

author = "Nils Bausch and Peter Shilling and David Sanders and Haddad, {Malik Jamal Musa} and Okonor, {Ogechukwu Mercy} and Giles Tewkesbury",

year = "2019",

month = aug,

day = "24",

doi = "10.1007/978-3-030-29516-5_54",

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pages = "721--739",

editor = "Yaxin Bi and Rahul Bhatia and Supriya Kapoor",

booktitle = "IntelliSys 2019 Intelligent Systems and Applications",

note = "IEEE SAI Intelligent Systems Conference 2019, IntelliSys 2019 ; Conference date: 05-09-2019 Through 06-09-2019",

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Bausch, N, Shilling, P, Sanders, D, Haddad, MJM, Okonor, OM & Tewkesbury, G 2019, Indoor location and collision feedback for a powered wheelchair system using machine learning. in Y Bi, R Bhatia & S Kapoor (eds), IntelliSys 2019 Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Systems Conference. vol. 1, Advances in Intelligent Systems and Computing, vol. 1037, Springer, pp. 721-739, IEEE SAI Intelligent Systems Conference 2019, London, United Kingdom, 5/09/19. https://doi.org/10.1007/978-3-030-29516-5_54

Indoor location and collision feedback for a powered wheelchair system using machine learning. / Bausch, Nils; Shilling, Peter; Sanders, David et al.
IntelliSys 2019 Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Systems Conference. ed. / Yaxin Bi; Rahul Bhatia; Supriya Kapoor. Vol. 1 Springer, 2019. p. 721-739 (Advances in Intelligent Systems and Computing; Vol. 1037).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Indoor location and collision feedback for a powered wheelchair system using machine learning

AU - Bausch, Nils

AU - Shilling, Peter

AU - Sanders, David

AU - Haddad, Malik Jamal Musa

AU - Okonor, Ogechukwu Mercy

AU - Tewkesbury, Giles

PY - 2019/8/24

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AB - 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.

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Bausch N, Shilling P, Sanders D, Haddad MJM, Okonor OM, Tewkesbury G. Indoor location and collision feedback for a powered wheelchair system using machine learning. In Bi Y, Bhatia R, Kapoor S, editors, IntelliSys 2019 Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Systems Conference. Vol. 1. Springer. 2019. p. 721-739. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-29516-5_54

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

Abstract

Publication series

Conference

Keywords

Access to Document

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Projects

Using artificial intelligence to share control of a powered-wheelchair between a wheelchair user and an intelligent sensor system

Cite this