Deep Learning architecture to assist with steering a powered wheelchair

Malik Haddad; David Sanders

doi:10.1109/TNSRE.2020.3031468

Deep Learning architecture to assist with steering a powered wheelchair

Malik Haddad, David Sanders

Research output: Contribution to journal › Article › peer-review

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Abstract

This paper describes a novel Deep Learning architecture to assist with steering a powered wheelchair. A rule-based approach is utilized to train and test a Long Short Term Memory (LSTM) Neural Network. It is the first time a LSTM has been used for steering a powered wheelchair. A disabled driver uses a joystick to provide desired speed and direction, and the Neural Network provides a safe direction for the wheelchair. Results from the Neural Network are mixed with desired speed and direction to avoid obstacles. Inputs originate from a joystick and from three ultrasonic transducers attached to the chair. The resultant course is a blend of desired directions and directions that steer the chair to avoid collision. A rule-based approach is used to create a training and test set for the Neural Network system and applies deep learning to predict a safe route for a wheelchair. The user can over-ride the new system if necessary.

Original language	English
Article number	9225000
Pages (from-to)	2987-2994
Number of pages	7
Journal	IEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume	28
Issue number	12
Early online date	15 Oct 2020
DOIs	https://doi.org/10.1109/TNSRE.2020.3031468
Publication status	Published - 1 Dec 2020

Keywords

Deep Learning
Wheelchair
Neural Network
Rule-based
Disabled
Steer
RCUK
EPSRC
EP/S005927/1

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10.1109/TNSRE.2020.3031468

Deep Learning Architecture
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Accepted author manuscript (Post-print), 890 KB

Dataset for 'Deep Learning Architecture to Assist with Steering a Powered Wheelchair'.
Haddad, M. (Creator), University of Portsmouth, 18 Nov 2020
DOI: 10.17029/432fa5e2-eff4-47f6-99a9-df5123cd8bee
Dataset

File

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

Top two per cent of researchers in the world
Sanders, David (Recipient), 2 Nov 0001
Prize: National/international honour

Cite this

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title = "Deep Learning architecture to assist with steering a powered wheelchair",

abstract = "This paper describes a novel Deep Learning architecture to assist with steering a powered wheelchair. A rule-based approach is utilized to train and test a Long Short Term Memory (LSTM) Neural Network. It is the first time a LSTM has been used for steering a powered wheelchair. A disabled driver uses a joystick to provide desired speed and direction, and the Neural Network provides a safe direction for the wheelchair. Results from the Neural Network are mixed with desired speed and direction to avoid obstacles. Inputs originate from a joystick and from three ultrasonic transducers attached to the chair. The resultant course is a blend of desired directions and directions that steer the chair to avoid collision. A rule-based approach is used to create a training and test set for the Neural Network system and applies deep learning to predict a safe route for a wheelchair. The user can over-ride the new system if necessary.",

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Deep Learning architecture to assist with steering a powered wheelchair

Abstract

Keywords

Access to Document

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Datasets

Dataset for 'Deep Learning Architecture to Assist with Steering a Powered Wheelchair'.

Projects

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

Prizes

Top two per cent of researchers in the world

Cite this