Erik Scheme

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

Using electromyogram (EMG) signals to control upper-limb prostheses is an important clinical option, offering a person with amputation autonomy of control by contracting residual muscles. The dexterity with which one may control a prosthesis has progressed very little, especially when controlling multiple degrees of freedom. Using pattern recognition to discriminate multiple degrees of freedom has shown great promise in the research literature, but it has yet to transition to a clinically viable option. This article describes the pertinent issues and best practices in EMG pattern recognition, identifies the major challenges in deploying robust control, and advocates research directions that may have an effect in the near future.

Resolving the Limb Position Effect in Myoelectric Pattern Recognition

Reported studies on pattern recognition of electromyograms (EMG) for the control of prosthetic devices traditionally focus on classification accuracy of signals recorded in a laboratory. The difference between the constrained nature in which such data are often collected and the unpredictable nature of prosthetic use is an example of the semantic gap between research findings and a viable clinical implementation. In this paper, we demonstrate that the variations in limb position associated with normal use can have a substantial impact on the robustness of EMG pattern recognition, as illustrated by an in- crease in average classification error from 3.8% to 18%. We propose to solve this problem by: 1) collecting EMG data and training the classifier in multiple limb positions and by 2) measuring the limb position with accelerometers. Applying these two methods to data from ten normally limbed subjects, we reduce the average classification error from 18% to 5.7% and 5.0%, respectively. Our study shows how sensor fusion (using EMG and accelerometers) may be an efficient method to mitigate the effect of limb position and improve classification accuracy.

Multiple Binary Classifications via Linear Discriminant Analysis for Improved Controllability of a Powered Prosthesis

This paper describes a novel pattern recognition based myoelectric control system that uses parallel binary classification and class specific thresholds. The system was designed with an intuitive configuration interface, similar to existing conventional myoelectric control systems. The system was assessed quantitatively with a classification error metric and functionally with a clothespin test implemented in a virtual environment. For each case, the proposed system was compared to a state-of-the-art pattern recognition system based on linear discriminant analysis and a conventional myoelectric control scheme with mode switching. These assessments showed that the proposed control system had a higher classification error (p < 0.001) but yielded a more controllable myoelectric control system (p < 0.001) as measured through a clothespin usability test implemented in a virtual environment. Furthermore, the system was computationally simple and applicable for real-time embedded implementation. This work provides the basis for a clinically viable pattern recognition based myoelectric control system which is robust, easily configured, and highly usable.

Examining the adverse effects of limb position on pattern recognition based myoelectric control

Pattern recognition of myoelectric signals for the control of prosthetic devices has been widely reported and debated. A large portion of the literature focuses on offline classification accuracy of pre-recorded signals. Historically, however, there has been a semantic gap between research findings and a clinically viable implementation. Recently, renewed focus on prosthetics research has pushed the field to provide more clinically relevant outcomes. One way to work towards this goal is to examine the differences between research and clinical results. The constrained nature in which offline training and test data is often collected compared to the dynamic nature of prosthetic use is just one example. In this work, we demonstrate that variations in limb position after training can have a substantial impact on the robustness of myoelectric pattern recognition.

Selective Classification for Improved Robustness of Myoelectric Control Under Nonideal Conditions

Recent literature in pattern recognition-based myoelectric control has highlighted a disparity between classification accuracy and the usability of upper limb prostheses. This paper suggests that the conventionally defined classification accuracy may be idealistic and may not reflect true clinical performance. Herein, a novel myoelectric control system based on a selective multiclass one-versus-one classification scheme, capable of rejecting unknown data patterns, is introduced. This scheme is shown to outperform nine other popular classifiers when compared using conventional classification accuracy as well as a form of leave-one-out analysis that may be more representative of real prosthetic use. Additionally, the classification scheme allows for real-time, independent adjustment of individual class-pair boundaries making it flexible and intuitive for clinical use.

Proceedings of the first workshop on peripheral machine interfaces: Going beyond traditional surface electromyography

One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive peripheral nervous system (PNS)–Machine Interfaces (MI; PMI) was convened, hosted by the International Conference on Rehabilitation Robotics. The keyword PMI has been selected to denote human–machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the PNS in a non-invasive way, that is, from the surface of the residuum. The workshop was intended to provide an overview of the state of the art and future perspectives of such interfaces; this paper represents is a collection of opinions expressed by each and every researcher/group involved in it.

Continuous Detection and Decoding of Dexterous Finger Flexions With Implantable MyoElectric Sensors

A rhesus monkey was trained to perform individuated and combined finger flexions of the thumb, index, and middle finger. Nine implantable myoelectric sensors (IMES) were then surgically implanted into the finger muscles of the monkeys forearm, without any adverse effects over two years postimplantation. Using an inductive link, EMG was wirelessly recorded from the IMES as the monkey performed a finger flexion task. The EMG from the different IMES implants showed very little cross correlation. An offline parallel linear discriminant analysis (LDA) based algorithm was used to decode finger activity based on features extracted from continuously presented frames of recorded EMG. The offline parallel LDA was run on intraday sessions as well as on sessions where the algorithm was trained on one day and tested on following days. The performance of the algorithm was evaluated continuously by comparing classification output by the algorithm to the current state of the finger switches. The algorithm detected and classified seven different finger movements, including individual and combined finger flexions, and a no-movement state (chance performance = 12.5%) . When the algorithm was trained and tested on data collected the same day, the average performance was 43.8±3.6% n=10. When the training-testing separation period was five months, the average performance of the algorithm was 46.5±3.4% n=8. These results demonstrated that using EMG recorded and wirelessly transmitted by IMES offers a promising approach for providing intuitive, dexterous control of artificial limbs where human patients have sufficient, functional residual muscle following amputation.

Real-Time, Simultaneous Myoelectric Control Using Force and Position-Based Training Paradigms

In this paper, the simultaneous real-time control of multiple degrees of freedom (DOF) for myoelectric systems is investigated. The goal of this study, in which ten able-bodied subjects participated, was to directly compare three control paradigms of constrained (force targeted), unconstrained (position targeted) and resisted unconstrained (position targeted) limb contractions. Artificial neural networks (ANNs) were trained for simultaneous myoelectric control of the three degrees of freedom (DOFs) (wrist flexion-extension, abduction-adduction, and pronation-supination) using mirrored bilateral contractions. In the resisted unconstrained experiment, some resistance to movement was provided using flexible wrist braces in order to increase the required level of muscle activation. The force, in constrained experiments, and position, in unconstrained and resisted unconstrained experiments, were measured. The three protocols were compared off-line using estimation accuracies (R2) and online using a real-time computer-based target acquisition test. The constrained control paradigm outperformed the unconstrained method in the abduction-adduction DOF (Rconstrained2 = 90.8 ± 0.6, Runconstrained2 = 85.6 ± 1.6) and pronation-supination DOF ( Rconstrained2 = 88.5 ± 0.9, Runconstrained2 = 82.3 ± 1.6), but no significant difference was found in the flexion-extension DOF. The constrained control method outperformed unconstrained control in two real-time testing metrics including completion time and path efficiency. The constrained method results, however, were not significantly different than those of the resisted unconstrained method (with braces) in both off-line and real-time tests. This suggests that the quality of control using constrained and unconstrained contraction-based myoelectric schemes is not appreciably different when using comparable levels of muscle activation.

Training Strategies for Mitigating the Effect of Proportional Control on Classification in Pattern Recognition Based Myoelectric Control.

ABSTRACT The performance of pattern recognition–based myoelectric control has seen significant interest in the research community for many years. Because of a recent surge in the development of dexterous prosthetic devices, determining the clinical viability of multifunction myoelectric control has become paramount. Several factors contribute to differences between offline classification accuracy and clinical usability, but the overriding theme is that the variability of the elicited patterns increases greatly during functional use. Proportional control has been shown to greatly improve the usability of conventional myoelectric control systems. Typically, a measure of the amplitude of the electromyogram (a rectified and smoothed version) is used to dictate the velocity of control of a device. The discriminatory power of myoelectric pattern classifiers, however, is also based largely on the amplitude features of the electromyogram. This work presents an introductory look at the effect of contraction strength and proportional control on pattern recognition–based control. These effects are investigated using typical pattern recognition data collection methods as well as a real-time position tracking test. Training with dynamically force varying contractions and appropriate gain selection is shown to significantly improve (p < 0.001) the classifier’s performance and tolerance to proportional control.

A multi-modal approach for hand motion classification using surface EMG and accelerometers

For decades, electromyography (EMG) has been used for diagnostics, upper-limb prosthesis control, and recently even for more general human-machine interfaces. Current commercial upper limb prostheses usually have only two electrode sites due to cost and space limitations, while researchers often experiment with multiple sites. Micro-machined inertial sensors are gaining popularity in many commercial and research applications where knowledge of the postures and movements of the body is desired. In the present study, we have investigated whether accelerometers, which are relatively cheap, small, robust to noise, and easily integrated in a prosthetic socket; can reduce the need for adding more electrode sites to the prosthesis control system. This was done by adding accelerometers to a multifunction system and also to a simplified system more similar to current commercially available prosthesis controllers, and assessing the resulting changes in classification accuracy. The accelerometer does not provide information on muscle force like EMG electrodes, but the results show that it provides useful supplementary information. Specifically, if one wants to improve a two-site EMG system, one should add an accelerometer affixed to the forearm rather than a third electrode.