A characterization of the effect of limb position on EMG features to guide the development of effective prosthetic control schemes

Abstract

Electromyogram (EMG) pattern recognition has long been used for the control of upper limb prostheses. More recently, it has been shown that variability induced during functional use, such as changes in limb position and dynamic contractions, can have a substantial impact on the robustness of EMG pattern recognition. This work further investigates the reasons for pattern recognition performance degradation due to the limb position variation. The main focus is on the impact of limb position variation on features of the EMG, as measured using separability and repeatability metrics. The results show that when the limb is moved to a position different from the one in which the classifier is trained, both the separability and repeatability of the data decrease. It is shown how two previously proposed classification methods, multiple position training and dual-stage classification, resolve the position effect problem to some extent through increasing either separability or repeatability but not both. A hybrid classification method which exhibits a compromise between separability and repeatability is proposed in this work. It is shown that, when tested with the limb in 16 different positions, this method increases classification accuracy from an average of 70% (single position training) to 89% (hybrid approach). This hybrid method significantly (p<;0.05) outperforms multiple position training (an average of 86%) and dual-stage classification (an average of 85%).

High-density force myography: A possible alternative for upper-limb prosthetic control

“…To study the performance of the HD-FMG technique, we compared the classification results with those of standard pattern recognition-based EMG control methods that have been reported in the literature [13][14][15][16][17][18], and followed a similar experimental protocol as that used by Radmand et al [28]. Subjects were prompted to elicit contractions corresponding to eight classes of motion including wrist flexion/extension, wrist supination/pronation, pinch grip, power grip, hand open, and no movement.…”

“…Subjects were instructed to perform contractions at a moderate and repeatable force Subjects were asked to perform four sets of contractions corresponding to eight classes of motion while holding their arm such that the hand was located in each of the eight static positions represented by the boxes numbered 1 through 8. Reprinted with permission from Radmand et al [28]. level and given rest periods between trials to avoid fatigue.…”

“…As first laid out in Radmand et al [28], "These positions cover the workspace in which most activities of daily living (ADL) are performed. To ensure that all subjects moved their arms to the same set of eight positions, the subjects were asked to stand in front of a white board, on which a grid of four cells corresponding to positions P1-P4 was drawn, and move the limb as if they want to reach the center of each cell.…”

“…By training the classifier using data from multiple arm positions and including samples from different positions in the training data set, this error can be reduced, as shown in Figure 10. This method has been previously shown to be effective for resolving the position effect for EMG control [16]; it increases the repeatability between the training and testing data sets [28]. Another proposed solution to the position effect problem, dynamic training, was not very effective for HD-FMG control because muscle geometry changes as the arm moves in space.…”

A Review of Intent Detection, Arbitration, and Communication Aspects of Shared Control for Physical Human–Robot Interaction

“…In Refs. [14][15][16][17], upper limb prosthetics are controlled by the user selecting one of these predefined functions; therefore, the user intent is represented by a single categorical variable.…”