Deep Learning architecture to assist with steering a powered wheelchair
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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.
| 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 | |
| Publication status | Published - 1 Dec 2020 |
Documents
- Deep Learning Architecture
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Accepted author manuscript (Post-print), 890 KB, PDF document
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