Using forearm EMG for path selection of a robotic wheelchair

by Kevin Kuo
Patients with motor control deficiencies due to muscle weakness, stroke, or motor neuron diseases can face difficulties using conventional joysticks. Such patients can benefit from alternative sensory inputs to inform control of a wheelchair. The objective of this work is to assist patients with steering by mapping forearm Electromyography (EMG) signals to joystick control. The methods in this work applied bandpass filter for preprocessing, and rolling variance with linear discriminant analysis for classification.
Major: 
Computer Science
Exhibition Category: 
Health and Life Sciences
Exhibition Format: 
Poster Presentation
Campus: 
University Park
Faculty Sponsor: 
Sean Brennan
Location: 
Alumni Hall, HUB-Robeson Center
Poster Number: 
241