A next generation neural prosthesis to improve gait in people with muscle weakness
- WCU Author/Contributor (non-WCU co-authors, if there are any, appear on document)
- Premkumar Subbukutti (Creator)
- Institution
- Western Carolina University (WCU )
- Web Site: http://library.wcu.edu/
- Advisor
- Martin Tanaka
Abstract: Some of the 5.3 million people in the US who are living with some form of paralysis may be assisted by a neural prosthesis that employs Functional Electrical Stimulation (FES). FES produces muscular contractions by applying an electrical stimulation to nerves that supply a muscle. The specific goal of this research was to develop a neural prosthesis capable of accurately detecting human gait characteristics to determine proper timing for artificial muscle stimulation. This third-generation neural prosthesis uses four force sensitive resistors, four inertial measurement units (IMUs), a Raspberry Pi microcontroller, and has improved data collection and storage software, real time data filtering and add wireless communication. Tests on a healthy individual were performed to evaluate the device’s ability to measure and record gait data. Collected data was compared to the data collected from the camera motion capture system to determine the device’s accuracy. Testing showed that the neural prosthesis was able to capture the general shape of the joint angle curves when compared to the camera motion capture system. However, the joint angles obtained from the neural prosthesis device lagged the actual joint angles found using the camera system. This is likely due to a slow response time in the gyroscope. In the future, measures will be taken to reduce lag in the gyroscope and reduce jitter in the accelerometer so that data from both sensors can be combination to obtain more accurate readings.
A next generation neural prosthesis to improve gait in people with muscle weakness
PDF (Portable Document Format)
3594 KB
Created on 4/1/2020
Views: 934
Additional Information
- Publication
- Thesis
- Language: English
- Date: 2020
- Keywords
- Complementary filter, FES, Gait cycle, Joint angles, Neural prosthesis, Raspberry PI
- Subjects
- Prosthesis
- Neuroprostheses
- Gait disorders
- Joints
- Raspberry Pi (Computer)