| dc.contributor.author | Schwandt, Daniel Jason | |
| dc.description.abstract | This paper proposes the integration of an image sensor classification system into the
control scheme for prosthetic limbs. The goal is to minimize the mental strain experienced
by brain-computer interface (BCI) users when performing routine tasks. In particular, the
focus is to investigate the capabilities of image classification as a method for identifying
and classifying objects. Incorporating this process into prosthesis control will allow more
fluid and natural movements while minimizing the mental strain of multiple commands by
the user. A popular imaging and classification software package was evaluated by
exposing it to various images under different conditions. Results indicate that an image
sensor classification system is suitable for prosthetic limb control in many situations;
however, the system performance is reduced in low light and clustered object conditions. It
is therefore feasible to consider the integration of image sensor classification and camerain-hand technology into EMG- and BCI-controlled prosthetics. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.rights | NDSU policy 190.6.2 | en_US |
| dc.title | Image Sensor Classification System for Prosthesis Control | en_US |
| dc.type | Master's Paper | en_US |
| dc.date.accessioned | 2024-01-05T18:50:48Z | |
| dc.date.available | 2024-01-05T18:50:48Z | |
| dc.date.issued | 2010 | |
| dc.identifier.uri | https://hdl.handle.net/10365/33577 | |
| dc.subject.lcsh | Myoelectric prosthesis. | en_US |
| dc.subject.lcsh | Image converters. | en_US |
| dc.subject.lcsh | Image processing -- Digital techniques. | en_US |
| dc.rights.uri | https://www.ndsu.edu/fileadmin/policy/190.pdf | en_US |
| ndsu.degree | Master of Science (MS) | en_US |
| ndsu.college | Engineering | en_US |
| ndsu.department | Electrical and Computer Engineering | en_US |
| ndsu.program | Electrical and Computer Engineering | en_US |
| ndsu.advisor | Schroeder, Mark J. | |
