Despite not recording directly from neural cells, the surface electromyogram (EMG) signal contains information on the neural drive to muscles, i.e., the spike trains of motor neurons. Using this property, myoelectric control consists of the recording of EMG signals for extracting control signals to command external devices, such as hand prostheses. In commercial control systems, the intensity of muscle activity is extracted from the EMG and used for single degrees of freedom activation (direct control). Over the past 60 years, academic research has progressed to more sophisticated approaches but, surprisingly, none of these academic achievements has been implemented in commercial systems so far. We provide an overview of both commercial and academic myoelectric control systems and we analyze their performance with respect to the characteristics of the ideal myocontroller. Classic and relatively novel academic methods are described, including techniques for simultaneous and proportional control of multiple degrees of freedom and the use of individual motor neuron spike trains for direct control. The conclusion is that the gap between industry and academia is due to the relatively small functional improvement in daily situations that academic systems offer, despite the promising laboratory results, at the expense of a substantial reduction in robustness. None of the systems so far proposed in the literature fulfills all the important criteria needed for widespread acceptance by the patients, i.e. intuitive, closed-loop, adaptive, and robust real-time ( 200 ms delay) control, minimal number of recording electrodes with low sensitivity to repositioning, minimal training, limited complexity and low consumption. Nonetheless, in recent years, important efforts have been invested in matching these criteria, with relevant steps forwards.

The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges

Surface EMG based muscle fatigue evaluation in biomechanics

https://users.wpi.edu/~ted/full_text/2002clancy_morin_jek.pdf

Sampling, noise-reduction and amplitude estimation issues in surface electromyography

The recent introduction of novel multifunction hands as well as new control paradigms increase the demand for advanced prosthetic control systems. In this context, an unambiguous terminology and a good understanding of the nature of the control problem is important for efficient research and communication concerning the subject. Thus, one purpose of this paper is to suggest an unambiguous taxonomy, applicable to control systems for upper limb prostheses and also to prostheses in general. A functionally partitioned model of the prosthesis control problem is also presented along with the taxonomy. In the second half of the paper, the suggested taxonomy has been exploited in a comprehensive literature review on proportional myoelectric control of upper limb prostheses. The review revealed that the methods for system training have not matured at the same pace as the novel multifunction prostheses and more advanced intent interpretation methods. Few publications exist regarding the choice of training method and the composition of the training data set. In this context, the notion of outcome measures is essential. By definition, system training involves optimization, and the quality of the results depends heavily on the choice of appropriate optimization criteria. In order to further promote the development of proportional myoelectric control, these topics need to be addressed.

/pdf/control-of-upper-limb-prostheses-terminology-and-3hc1g395j4.pdf

Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control—A Review

Several efforts have been carried out to enhance dexterous hand prosthesis control by impaired individuals. Choosing which voluntary signal to use for control purposes is a critical element to achieve this goal. This review presents and discusses the recent results achieved by using electromyographic signals, recorded either with surface (sEMG) or intramuscular (iEMG) electrodes, and electroneurographic (ENG) signals. The potential benefits and shortcomings of the different approaches are described with a particular attention to the definition of all the steps required to achieve an effective hand prosthesis control in the different cases. Finally, a possible roadmap in the field is also presented.

/pdf/control-of-hand-prostheses-using-peripheral-information-3amjn63ibu.pdf

Control of Hand Prostheses Using Peripheral Information

Proportional myoelectric control of powered prostheses requires the estimation of a time-varying control signal from the patient's myoelectric signal. Since the myoelectric signal is a zero-mean stochastic process, a nonlinearity is a necessary element of the estimator. Typically, a full-wave rectifier is used for this nonlinearity, followed by a low-pass filter to complete the estimation of the control signal. In this work, it is proposed to use a logarithmic nonlinearity, followed by a linear minimum mean-square error estimator. The logarithmic nonlinearity maps the myoelectric signal into an additive control signal-plus-noise domain in which the Kalman filter is employed to estimate the control signal. The theoretical performance of this estimator is obtained and verified by experiments.

Harry B. Evans

Papers

Signal Processing for Proportional Myoelectric Control