Abstract
Myoelectric pattern recognition using linear discriminant analysis (LDA) classifiers has been a wellestablished control method for upper limb prostheses for many years. More recently, linear regression (LR) controllers have been proposed as an alternative solution due to their ability to control multiple degrees of freedom (DOF) simultaneously. The aim of this experiment was to compare the online performance of LDA and LR control systems under three electromyographic (EMG) signal conditions: baseline, noise in all channels, and noise in a single channel. To simulate the last two conditions, different levels of Gaussian noise were added to the EMG signals. Completion rate, path efficiency, dwelling time, and completion time were computed after virtual Fitts' Law tasks. While both controllers were significantly affected by the lowest noise levels, we found no significant differences between the controllers under the baseline and all-channel noise conditions. However, the LDA controller outperformed the LR controller in the single-channel noise condition. Therefore, while both controllers are comparable in most cases, the added complexity of simultaneous control affects an LR controller's performance under certain noise conditions. Based on these results, neither control system should be dismissed in future developments.
| Original language | English (US) |
|---|---|
| Title of host publication | 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2132-2135 |
| Number of pages | 4 |
| Volume | 2018-July |
| ISBN (Electronic) | 9781538636466 |
| DOIs | |
| State | Published - Oct 26 2018 |
| Event | 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States Duration: Jul 18 2018 → Jul 21 2018 |
Other
| Other | 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 |
|---|---|
| Country | United States |
| City | Honolulu |
| Period | 7/18/18 → 7/21/18 |
Fingerprint
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics
Cite this
}
Comparing the Effects of Signal Noise on Pattern Recognition and Linear Regression-Based Myoelectric Controllers. / Teh, Yuni; Woodward, Richard B.; Hargrove, Levi J.
40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Vol. 2018-July Institute of Electrical and Electronics Engineers Inc., 2018. p. 2132-2135 8512693.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Comparing the Effects of Signal Noise on Pattern Recognition and Linear Regression-Based Myoelectric Controllers
AU - Teh, Yuni
AU - Woodward, Richard B.
AU - Hargrove, Levi J
PY - 2018/10/26
Y1 - 2018/10/26
N2 - Myoelectric pattern recognition using linear discriminant analysis (LDA) classifiers has been a wellestablished control method for upper limb prostheses for many years. More recently, linear regression (LR) controllers have been proposed as an alternative solution due to their ability to control multiple degrees of freedom (DOF) simultaneously. The aim of this experiment was to compare the online performance of LDA and LR control systems under three electromyographic (EMG) signal conditions: baseline, noise in all channels, and noise in a single channel. To simulate the last two conditions, different levels of Gaussian noise were added to the EMG signals. Completion rate, path efficiency, dwelling time, and completion time were computed after virtual Fitts' Law tasks. While both controllers were significantly affected by the lowest noise levels, we found no significant differences between the controllers under the baseline and all-channel noise conditions. However, the LDA controller outperformed the LR controller in the single-channel noise condition. Therefore, while both controllers are comparable in most cases, the added complexity of simultaneous control affects an LR controller's performance under certain noise conditions. Based on these results, neither control system should be dismissed in future developments.
AB - Myoelectric pattern recognition using linear discriminant analysis (LDA) classifiers has been a wellestablished control method for upper limb prostheses for many years. More recently, linear regression (LR) controllers have been proposed as an alternative solution due to their ability to control multiple degrees of freedom (DOF) simultaneously. The aim of this experiment was to compare the online performance of LDA and LR control systems under three electromyographic (EMG) signal conditions: baseline, noise in all channels, and noise in a single channel. To simulate the last two conditions, different levels of Gaussian noise were added to the EMG signals. Completion rate, path efficiency, dwelling time, and completion time were computed after virtual Fitts' Law tasks. While both controllers were significantly affected by the lowest noise levels, we found no significant differences between the controllers under the baseline and all-channel noise conditions. However, the LDA controller outperformed the LR controller in the single-channel noise condition. Therefore, while both controllers are comparable in most cases, the added complexity of simultaneous control affects an LR controller's performance under certain noise conditions. Based on these results, neither control system should be dismissed in future developments.
UR - http://www.scopus.com/inward/record.url?scp=85056630986&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056630986&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2018.8512693
DO - 10.1109/EMBC.2018.8512693
M3 - Conference contribution
VL - 2018-July
SP - 2132
EP - 2135
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -