Deep Learning Architecture to Assist With Steering a Powered Wheelchair

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 use...

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Bibliographic Details
Published inIEEE transactions on neural systems and rehabilitation engineering Vol. 28; no. 12; pp. 2987 - 2994
Main Authors Haddad, Malik J., Sanders, David A.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.12.2020
Subjects
Acoustics
Deep learning
disabled
neural network
rule-based
Sensor systems
steer
wheelchair
Wheelchairs
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Summary: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.
ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2020.3031468