A comparison between force and position control strategies in myoelectric prostheses

Dimensionality Reduction in EMG-Based Estimation of Wrist Kinematics

Pattern recognition has shown remarkable success in decoding motor information from electromyogram (EMG) signals. To decrease the computational complexity in EMG pattern recognition, it may be useful to reduce the dimensionality of the model input. This paper investigates the effect of reducing the dimensionality of EMG features in a regression-based motion intent estimation model. Ten able-bodied subjects participated in this analytic study. EMG signals from the right forearm and angle of the left wrist in three degrees of freedom (DoF) were measured, concurrently. The TD features were extracted from eight EMG channels, resulting in a total of 32 features. Three dimensionality reduction methods including principal component analysis (PCA), non-negative matrix factorization (NNMF), and canonical correlation analysis (CCA) were applied to the EMG features. Reducing the dimension of the EMG features below a certain threshold degraded the performance of the EMG pattern recognition model. Otherwise, dimensionality reduction did not change the performance. These thresholds for the PCA, NNMF, and CCA methods were 25, 26, and 13, respectively. Based on the results, CCA substantially outperformed PCA and NNMF, as it allowed a significant reduction of the EMG features size, from 32 to 13, with no adverse impact on the performance.

Recent Advances in EMG Pattern Recognition for Prosthetic Control

Limb loss results in significant debilitation and reduces the quality of life of the affected individuals [ 1 ]. To restore the lost limb’s function, myoelectric systems have been widely used in powered prostheses [ 2 ]. With this approach, the motor intent is estimated from the electromyogram (EMG) signals recorded by electrodes which are placed on the skin surface above the residual muscles [ 1 ]. The principle of commercial myoelectric schemes has not changed in several decades, and is referred to as conventional control [ 2 ]. This technique uses a measure of amplitude (such as mean absolute value over a time window) of the EMG signals recorded by electrodes placed at two control sites, preferably over a pair of antagonist muscles of the residual limb, to control a single motion i.e. degree of freedom (DoF), for example hand opening closing [ 2 ]. To change the DoF, a mode switch is conducted by muscle co-contraction or a hardware switch [ 2 ]. The mode switch, however, results in an unnatural control of multiple DoFs [ 2 ]. To overcome this challenge, a significant body of research has been conducted on pattern recognition techniques [ 3 ]. With this approach, a classifier is trained to discriminate between different DoFs, using patterns from multi-channel EMG input data. Promising results have been achieved in the literature for classification of several DoFs [ 2 ]. Since activities of daily living include simultaneous movements of multiple DoFs, combined motions must be also included as separate classes, and they have to be conducted in the training set [ 4 ]. The limitation of this approach, however, is that it does not allow the DoFs in combined motions to have different magnitudes. As a solution to this problem, regression-based systems have been proposed [ 5 , 6 ], where a regressor is trained to estimate each DoF, using data from single and combined motions. This strategy provides independent simultaneous control, because it does not limit the DoFs to have the same amplitude. Classification and regression based systems are the two categories of pattern recognition methods. Due to the high dimensionality of EMG signals, the EMG instantaneous values are not directly used as the inputs to classifiers/regressors [ 1 ]. Instead, a set of features is extracted from a time window (100-200 ms) of EMG signals [ 7 ]. Feature engineering is the process of design and extraction of features with the highest amount of useful information to maximize the classification/regression accuracy [ 8 ] Among various EMG features proposed in the literature, the Time Domain (TD) set [ 9 ] is the most popular set and includes mean absolute value, waveform length, zero-crossings, and slope sign changes. The past few years have seen the advent of deep learning-based myoelectric control [ 4 , 10 ]. Deep learning can perform classification/regression tasks directly from high-dimensional raw data, without feature engineering [ 8 ]. Convolutional neural network (CNN) [ 11 ] is one of the most widely used deep learning frameworks. The successive convolution layers of CNNs can learn useful features from the EMG data to estimate the motor intent [ 4 ]. As the outcomes of the previous studies [ 4 , 10 ] confirm, CNNs outperform classical models such as support vector machines (SVMs) with engineered feature sets. EMG pattern recognition schemes have yet to be deployed in commercial prostheses. The major challenge is performance degradation due to disturbances such as electrode shift, skin impedance change, muscle size variations, and learning effect [ 2 ]. Recent studies (e.g. [ 12 , 13 ]) have proposed methods to improve the robustness of EMG pattern recognition to such disturbances. These methods as well as new deep learning schemes that eliminate feature engineering, may pave the way for commercial implementation of myoelectric pattern recognition prostheses. Moreover, independent simultaneous control can be achieved by using regression deep learning models. These promising methods have the potential to significantly outperform existing commercial systems. Consequently, the missing functions in people with limb loss can be restored more efficiently by delivering a more natural and intuitive control.

Myoelectric Control for Upper Limb Prostheses

State-of-the-art high-end prostheses are electro-mechanically able to provide a great variety of movements. Nevertheless, in order to functionally replace a human limb, it is essential that each movement is properly controlled. This is the goal of prosthesis control, which has become a growing research field in the last decades, with the ultimate goal of reproducing biological limb control. Therefore, exploration and development of prosthesis control are crucial to improve many aspects of an amputee’s life. Nowadays, a large divergence between academia and industry has become evident in commercial systems. Although several studies propose more natural control systems with promising results, basic one degree of freedom (DoF), a control switching system is the most widely used option in industry because of simplicity, robustness and inertia. A few classification controlled prostheses have emerged in the last years but they are still a low percentage of the used ones. One of the factors that generate this situation is the lack of robustness of more advanced control algorithms in daily life activities outside of laboratory conditions. Because of this, research has shifted towards more functional prosthesis control. This work reviews the most recent literature in upper limb prosthetic control. It covers commonly used variants of possible biological inputs, its processing and translation to actual control, mostly focusing on electromyograms as well as the problems it will have to overcome in near future.

A Novel Spatial Feature for the Identification of Motor Tasks Using High-Density Electromyography

Estimation of neuromuscular intention using electromyography (EMG) and pattern recognition is still an open problem. One of the reasons is that the pattern-recognition approach is greatly influenced by temporal changes in electromyograms caused by the variations in the conductivity of the skin and/or electrodes, or physiological changes such as muscle fatigue. This paper proposes novel features for task identification extracted from the high-density electromyographic signal (HD-EMG) by applying the mean shift channel selection algorithm evaluated using a simple and fast classifier-linear discriminant analysis. HD-EMG was recorded from eight subjects during four upper-limb isometric motor tasks (flexion/extension, supination/pronation of the forearm) at three different levels of effort. Task and effort level identification showed very high classification rates in all cases. This new feature performed remarkably well particularly in the identification at very low effort levels. This could be a step towards the natural control in everyday applications where a subject could use low levels of effort to achieve motor tasks. Furthermore, it ensures reliable identification even in the presence of myoelectric fatigue and showed robustness to temporal changes in EMG, which could make it suitable in long-term applications.

Control of robotic assistance using poststroke residual voluntary effort

Poststroke hemiparesis limits the ability to reach, in part due to involuntary muscle co-activation (synergies). Robotic approaches are being developed for both therapeutic benefit and continuous assistance during activities of daily living. Robotic assistance may enable participants to exert less effort, thereby reducing expression of the abnormal co-activation patterns, which could allow participants to reach further. This study evaluated how well participants could perform a reaching task with robotic assistance that was either provided independent of effort in the vertical direction or in the sagittal plane in proportion to voluntary effort estimated from electromyograms (EMG) on the affected side. Participants who could not reach targets without assistance were enabled to reach further with assistance. Constant anti-gravity force assistance that was independent of voluntary effort did not reduce the quality of reach and enabled participants to exert less effort while maintaining different target locations. Force assistance that was proportional to voluntary effort on the affected side enabled participants to exert less effort and could be controlled to successfully reach targets, but participants had increased difficulty maintaining a stable position. These results suggest that residual effort on the affected side can produce an effective command signal for poststroke assistive devices.

System training and assessment in simultaneous proportional myoelectric prosthesis control

Background

Pattern recognition control of prosthetic hands take inputs from one or more myoelectric sensors and controls one or more degrees of freedom. However, most systems created allow only sequential control of one motion class at a time. Additionally, only recently have researchers demonstrated proportional myoelectric control in such systems, an option that is believed to make fine control easier for the user. Recent developments suggest improved reliability if the user follows a so-called prosthesis guided training (PGT) scheme.

Methods

In this study, a system for simultaneous proportional myoelectric control has been developed for a hand prosthesis with two motor functions (hand open/close, and wrist pro-/supination). The prosthesis has been used with a prosthesis socket equivalent designed for normally-limbed subjects. An extended version of PGT was developed for use with proportional control. The control system’s performance was tested for two subjects in the Clothespin Relocation Task and the Southampton Hand Assessment Procedure (SHAP). Simultaneous proportional control was compared with three other control strategies implemented on the same prosthesis: mutex proportional control (the same system but with simultaneous control disabled), mutex on-off control, and a more traditional, sequential proportional control system with co-contractions for state switching.

Results

The practical tests indicate that the simultaneous proportional control strategy and the two mutex-based pattern recognition strategies performed equally well, and superiorly to the more traditional sequential strategy according to the chosen outcome measures.

Conclusions

This is the first simultaneous proportional myoelectric control system demonstrated on a prosthesis affixed to the forearm of a subject. The study illustrates that PGT is a promising system training method for proportional control. Due to the limited number of subjects in this study, no definite conclusions can be drawn.