Limb-position robust classification of myoelectric signals for prosthesis control using sparse representations

Abstract

The fundamental objective in non-invasive myoelectric prosthesis control is to determine the user's intended movements from corresponding skin-surface recorded electromyographic (sEMG) activation signals as quickly and accurately as possible. Linear Discriminant Analysis (LDA) has emerged as the de facto standard for real-time movement classification due to its ease of use, calculation speed, and remarkable classification accuracy under controlled training conditions. However, performance of cluster-based methods like LDA for sEMG pattern recognition degrades significantly when real-world testing conditions do not resemble the trained conditions, limiting the utility of myoelectrically controlled prosthesis devices. We propose an enhanced classification method that is more robust to generic deviations from training conditions by constructing sparse representations of the input data dictionary comprised of sEMG time-frequency features. We apply our method in the context of upper-limb position changes to demonstrate pattern recognition robustness and improvement over LDA across discrete positions not explicitly trained. For single position training we report an accuracy improvement in untrained positions of 7.95%, p ≪ .001, in addition to significant accuracy improvements across all multiposition training conditions, p <; .001.

Fig. 1.

Training environment set-up. The subject is presented with a position indicator randomly chosen within a grid of 9 possible positions. Once the target position is acquired, the subject is presented with movement cues while sEMG data are collected.

Fig. 2.

Position-variant performance fall-off from single-position training. (a) LDA (left) vs. SFT₁ (right) per-position accuracy when trained in side position 4 and tested in all positions, with underlying visualization of the per-position class confusion. (b) The same analysis but for corner position 9. (c) Classifier confusion example trained in side position 4 and tested in position 6. In all cases, SFT₁ demonstrated less asymmetric fall-off and class confusion than LDA within our 9-position experiment space. The confusion of SFT₁ was often limited to a single class. In the specific examples shown, both algorithms are supplied with the same train/test dataset for classification.

Fig. 3.

Classifier comparison from our limb-position experiment. Each combination of k positions were used for training and the classifiers were tested in all 9 positions. SFT₁ significantly outperforms LDA in untrained positions. Lower k-values correspond to greater train/test asymmetry.