Determining the optimal window length for pattern recognition-based myoelectric control: balancing the competing effects of classification error and controller delay - PubMed (original) (raw)
Determining the optimal window length for pattern recognition-based myoelectric control: balancing the competing effects of classification error and controller delay
Lauren H Smith et al. IEEE Trans Neural Syst Rehabil Eng. 2011 Apr.
Abstract
Pattern recognition-based control of myoelectric prostheses has shown great promise in research environments, but has not been optimized for use in a clinical setting. To explore the relationship between classification error, controller delay, and real-time controllability, 13 able-bodied subjects were trained to operate a virtual upper-limb prosthesis using pattern recognition of electromyogram (EMG) signals. Classification error and controller delay were varied by training different classifiers with a variety of analysis window lengths ranging from 50 to 550 ms and either two or four EMG input channels. Offline analysis showed that classification error decreased with longer window lengths (p < 0.01 ). Real-time controllability was evaluated with the target achievement control (TAC) test, which prompted users to maneuver the virtual prosthesis into various target postures. The results indicated that user performance improved with lower classification error (p < 0.01 ) and was reduced with longer controller delay (p < 0.01 ), as determined by the window length. Therefore, both of these effects should be considered when choosing a window length; it may be beneficial to increase the window length if this results in a reduced classification error, despite the corresponding increase in controller delay. For the system employed in this study, the optimal window length was found to be between 150 and 250 ms, which is within acceptable controller delays for conventional multistate amplitude controllers.
Figures
Fig. 1
EMG data are analyzed using analysis windows of length Ta and window increments of length Tinc. The processing delay of the algorithm is represented as Td.
Fig. 2
(a) Classification error averaged across all subjects and trials (grouped by experimental session and number of channels). (b) Completion rate averaged across all subjects and trials (grouped by experimental session and number of channels). Error bars denote 1 standard deviation. S1 = Session 1, S2 = Session 2, C2 = two-channel classifier, C4 = four-channel classifier.
Fig. 3
Linear mixed-effects model parameters representing the increase in completion rate percentage observed when using the associated window length. All parameters are normalized to the completion rate achieved with 550 ms windows. Asterisks represent p-values resulting from a pair-wise comparison of window length values as predictors of completion rates. Single asterisk (*) represents p<0.05; double asterisk (**) represents p<0.01.
Fig. 4
The mean classification error for the two-channel (a) and four-channel (b) conditions was used with the linear mixed-effects model parameters for window length effect and classification error effect to determine the combined effect of the two variables on the completion rate. The data was normalized to the 550 ms window length condition.
References
- Ziegler-Graham K, MacKenzie EJ, Ephraim PL, Travison TG, Brookmeyer R. Estimating the prevalence of limb loss in the United States: 2005 to 2050. Archives of Physical Medicine and Rehabilitation. 2008 Mar;89:422–429. -PubMed
- Scott RN, Parker PA. Myoelectric Prostheses - State of the Art. Journal of medical engineering & technology. 1988;12:143–151. -PubMed
- Paciga JE, Richard PD, Scott RN. Error rate in five-state myoelectric control systems. Medical & biological engineering & computing. 1980;18:287–290. -PubMed
- Parker P, Stuller J, Scott RN. Signal Processing for the Multistate Myoelectric Channel. Proceedings of the IEEE. 1977;65:662–674.
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