Eye-Gaze Controlled Wheelchair Based on Deep Learning.

Journal: Sensors (Basel, Switzerland)
Published Date:

Abstract

In this paper, we design a technologically intelligent wheelchair with eye-movement control for patients with ALS in a natural environment. The system consists of an electric wheelchair, a vision system, a two-dimensional robotic arm, and a main control system. The smart wheelchair obtains the eye image of the controller through a monocular camera and uses deep learning and an attention mechanism to calculate the eye-movement direction. In addition, starting from the relationship between the trajectory of the joystick and the wheelchair speed, we establish a motion acceleration model of the smart wheelchair, which reduces the sudden acceleration of the smart wheelchair during rapid motion and improves the smoothness of the motion of the smart wheelchair. The lightweight eye-movement recognition model is transplanted into an embedded AI controller. The test results show that the accuracy of eye-movement direction recognition is 98.49%, the wheelchair movement speed is up to 1 m/s, and the movement trajectory is smooth, without sudden changes.

Authors

  • Jun Xu
    Department of Nephrology, The Affiliated Baiyun Hospital of Guizhou Medical University, Guizhou, China.
  • Zuning Huang
    School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, China.
  • Liangyuan Liu
    School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, China.
  • Xinghua Li
    School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, China.
  • Kai Wei
    School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, China.