Determining the Optimal Window Length for Pattern Recognition-Based Myoelectric Control: Balancing the Competing Effects of Classification Error and Controller Delay

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 multi-state amplitude controllers.

Determining delay created by multifunctional prosthesis controllers

“…While any delay in the controller caused a decrease in performance, this decrease was not found to be statistically or clinically significant until at least 100 to 125 ms of delay was present for an above-average (90th percentile) user. Therefore, we proposed that the controller delay that allowed the most time for signal collection and analysis (to maximize classification accuracy) without significantly decreasing prosthesis performance was found to be approximately 100 ms. Our results support Smith et al's recently completed study that took the experiment a step further by examining the effects of window length on both classification accuracy and controller delay and then determining how these two factors combined to affect performance on virtual tasks [20]. Smith et al concluded that, depending on the performance metric, the optimal controller delay ranged from 88 to 138 ms, which correlates well with our findings.…”

“…The combinations of analysis window lengths and majority votes were chosen with use of the previously derived equations, and each of these combinations was chosen to keep the controller delay below the 100 ms value (i.e., worst case is 100 ms) that Farrell and Weir [19] and Smith et al [20] determined. Analysis window lengths of 160, 120, 80, 40, and 20 ms were investigated along with their corresponding window shifts/ processing times and number of majority votes.…”

sEMG-Based Gesture Recognition with Convolution Neural Networks

“…As described in [14], to satisfy the requirement of real-time control, the time latency is less than 300 ms. The more extended window lengths led to higher controller delays as well as increased classification accuracy [42][43][44]. In previous works [13,40,45], L is greater than 200 ms to get higher classification accuracy.…”

Analysis of myoelectric signals using a Field Programmable SoC

“…MES data is non-stationary and stochastic in nature therefore most of the related analyses apply processing windows to extract descriptive features of the signal. In the current implementation a 150 ms long processing window was used because it has been shown that this length enables optimal performance for this type of classifiers [1].…”