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.
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U2 - 10.1109/EMBC.2018.8512693
DO - 10.1109/EMBC.2018.8512693
M3 - Conference contribution
C2 - 30440825
AN - SCOPUS:85056630986
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
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.
T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Y2 - 18 July 2018 through 21 July 2018
ER -