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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.
|Number of pages||7|
|Journal||IEEE Transactions on Neural Systems and Rehabilitation Engineering|
|Early online date||15 Oct 2020|
|Publication status||Published - 1 Dec 2020|
- Deep Learning
- Neural Network
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Haddad, M. (Creator), University of Portsmouth, 18 Nov 2020
- 1 Active
Using artificial intelligence to share control of a powered-wheelchair between a wheelchair user and an intelligent sensor system
1/11/18 → 1/11/22