Adaptive Pattern Recognition of Myoelectric Signals: Exploration of Conceptual Framework and Practical Algorithms

Abstract

Pattern Recognition is a useful tool for deciphering movement intent from myoelectric signals. Recognition paradigms must adapt with the user in order to be clinically viable over time. Most existing paradigms are static, although two forms of adaptation have received limited attention. Supervised adaptation can achieve high accuracy since the intended class is known, but at the cost of repeated cumbersome training sessions. Unsupervised adaptation attempts to achieve high accuracy without knowledge of the intended class, thus achieving adaptation that is not cumbersome to the user, but at the cost of reduced accuracy. This study reports a novel adaptive experiment on eight subjects that allowed repeated measures post-hoc comparison of four supervised and three unsupervised adaptation paradigms. All supervised adaptation paradigms reduced error over time by at least 26% compared to the nonadapting classifier. Most unsupervised adaptation paradigms provided smaller reductions in error, due to frequent uncertainty of the correct class. One method that selected high-confidence samples showed the most practical implementation, although the other methods warrant future investigation. Supervised adaptation should be considered for incorporation into any clinically viable pattern recognition controller, and unsupervised adaptation should receive renewed interest in order to provide transparent adaptation.

Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use

“…Although a pattern-recognition system may be calibrated upon donning, the nature of th ese effects is unpredictable, and therefore, the system must adapt to the changes in EMG. Adaptive EMG pattern-recognition systems have been investigated by several groups [67][68][69][70]. Adaptation of a pa ttern classifier is challenging because the system must know not only how to ad apt but also when to adapt.…”

Myoelectric forearm prostheses: State of the art from a user-centered perspective

“…Among many other features, amplitude features (mean absolute value and root-mean-square value) are often used to distinguish different contraction patterns (ranging from 4 to 8) and high accuracies (between 92% and 99%) can be achieved with them [20][21][22][23][24]. Examples of frequently used classifiers in the literature are linear discriminant analysis [22,[25][26][27][28] and artificial neural networks [29][30][31]. The highest achieved accuracies for both classifiers were around 98 percent.…”

“…Studies by Tenore et al [35] and Sebelius et al [21,29] incorporated flexion and extension of the separate fingers and thumb in classification. In recent studies, more focus is found on functional grasps, such as the cylindrical, tripod, and lateral grasps [28,[36][37][38].…”

Estimation of Finger Joint Angles from sEMG Using a Neural Network Including Time Delay Factor and Recurrent Structure

“…In research that targets estimating hand state from sEMG, many studies have focused on discriminating hand patterns [29][30][31][32][33][34]; that is, they deal with discrete clustering problems using sEMG as input information. The resultant systems can recognize hand shape (e.g., "open", "fist," or "peace sign"), and the main aim now is to apply such systems to the problem of controlling a prosthetic hand.…”