BackgroundElectromyography (EMG) pattern-recognition based control strategies for multifunctional myoelectric prosthesis systems have been studied commonly in a controlled laboratory setting. Before these myoelectric prosthesis systems are clinically viable, it will be necessary to assess the effect of some disparities between the ideal laboratory setting and practical use on the control performance. One important obstacle is the impact of arm position variation that causes the changes of EMG pattern when performing identical motions in different arm positions. This study aimed to investigate the impacts of arm position variation on EMG pattern-recognition based motion classification in upper-limb amputees and the solutions for reducing these impacts.MethodsWith five unilateral transradial (TR) amputees, the EMG signals and tri-axial accelerometer mechanomyography (ACC-MMG) signals were simultaneously collected from both amputated and intact arms when performing six classes of arm and hand movements in each of five arm positions that were considered in the study. The effect of the arm position changes was estimated in terms of motion classification error and compared between amputated and intact arms. Then the performance of three proposed methods in attenuating the impact of arm positions was evaluated.ResultsWith EMG signals, the average intra-position and inter-position classification errors across all five arm positions and five subjects were around 7.3% and 29.9% from amputated arms, respectively, about 1.0% and 10% low in comparison with those from intact arms. While ACC-MMG signals could yield a similar intra-position classification error (9.9%) as EMG, they had much higher inter-position classification error with an average value of 81.1% over the arm positions and the subjects. When the EMG data from all five arm positions were involved in the training set, the average classification error reached a value of around 10.8% for amputated arms. Using a two-stage cascade classifier, the average classification error was around 9.0% over all five arm positions. Reducing ACC-MMG channels from 8 to 2 only increased the average position classification error across all five arm positions from 0.7% to 1.0% in amputated arms.ConclusionsThe performance of EMG pattern-recognition based method in classifying movements strongly depends on arm positions. This dependency is a little stronger in intact arm than in amputated arm, which suggests that the investigations associated with practical use of a myoelectric prosthesis should use the limb amputees as subjects instead of using able-body subjects. The two-stage cascade classifier mode with ACC-MMG for limb position identification and EMG for limb motion classification may be a promising way to reduce the effect of limb position variation on classification performance.

Upper-limb amputation imposes significant burden on amputees thereby restricting them from fully exploring their environments during activities of daily living. The use of intelligent learning algorithm for electromyogram-pattern recognition (EMG-PR)-based control in upper-limb prostheses is considered as an important clinical option. Though the existing EMG-PR prostheses could discriminate multiple degrees of freedom (DOF) limb movements, their transition to clinically viable option is still being challenged by some confounding factors. Toward realizing a clinically viable multiple DOF prostheses, this paper first explored the principles and dynamics of the existing intelligently driven EMG-PR-based prostheses control scheme. Then, investigations on core issues including variation in muscle contraction force, electrode shift, and subject mobility affecting the existing EMG-PR prosthetic control scheme were reported. For instance, variation in muscle contraction force and subject mobility led to degradation in the performance of the EMG-PR controlled prostheses with approximately 17.00% and 8.98% error values, respectively, which are still challenging issues among others. Thus, this paper reports core issues and best practices with respect to intelligent EMG-PR controlled prosthesis, the major challenges in implementing adaptively robust control scheme and provides future research directions that may result in the clinical realization of intuitively dexterous multiple DOF EMG-PR-based prostheses in the near future.

Previous studies have showed that arm position variations would significantly degrade the classification performance of myoelectric pattern-recognition-based prosthetic control, and the cascade classifier (CC) and multiposition classifier (MPC) have been proposed to minimize such degradation in offline scenarios. However, it remains unknown whether these proposed approaches could also perform well in the clinical use of a multifunctional prosthesis control. In this study, the online effect of arm position variation on motion identification was evaluated by using a motion-test environment (MTE) developed to mimic the real-time control of myoelectric prostheses. The performance of different classifier configurations in reducing the impact of arm position variation was investigated using four real-time metrics based on dataset obtained from transradial amputees. The results of this study showed that, compared to the commonly used motion classification method, the CC and MPC configurations improved the real-time performance across seven classes of movements in five different arm positions (8.7% and 12.7% increments of motion completion rate, resp.). The results also indicated that high offline classification accuracy might not ensure good real-time performance under variable arm positions, which necessitated the investigation of the real-time control performance to gain proper insight on the clinical implementation of EMG-pattern-recognition-based controllers for limb amputees.

Historically, the investigations of electromyography (EMG) pattern recognition-based classification of intentional movements for control of multifunctional prostheses have adopted the filter cut-off frequency and sampling rate that are commonly used in EMG research fields. In practical implementation of a multifunctional prosthesis control, it is desired to have a higher high-pass cut-off frequency to reduce more motion artifacts and to use a lower sampling rate to save the data processing time and memory of the prosthesis controller. However, it remains unclear whether a high high-pass cut-off frequency and a low-sampling rate still preserve sufficient neural control information for accurate classification of movements. In this study, we investigated the effects of high-pass cut-off frequency and sampling rate on accuracy in identifying 11 classes of arm and hand movements in both able-bodied subjects and arm amputees. Compared to a 5-Hz high-pass cut-off frequency, excluding the EMG components below 60 Hz decreased the average accuracy of 0.1% in classifying the 11 movements across able-bodied subjects and increased the average accuracy of 0.1 and 0.4% among the transradial (TR) and shoulder disarticulation (SD) amputees, respectively. Using a 500 Hz instead of a 1-kHz sampling rate, the average classification accuracy only dropped about 2.0% in arm amputees. The combination of sampling rate and high-pass cut-off frequency of 500 and 60 Hz only resulted in about 2.3% decrease in average accuracy for TR amputees and 0.4% decrease for SD amputees in comparison to the generally used values of 1 kHz and 5 Hz. These results suggest that the combination of sampling rate of 500 Hz and high-pass cut-off frequency of 60 Hz should be an optimal selection in EMG recordings for recognition of different arm movements without sacrificing too much of classification accuracy which can also remove most of motion artifacts and power-line interferences for improving the performance of myoelectric prosthesis control.

BackgroundSelecting an appropriate number of surface electromyography (EMG) channels with desired classification performance and determining the optimal placement of EMG electrodes would be necessary and important in practical myoelectric control. In previous studies, several methods such as sequential forward selection (SFS) and Fisher-Markov selector (FMS) have been used to select the appropriate number of EMG channels for a control system. These exiting methods are dependent on either EMG features and/or classification algorithms, which means that when using different channel features or classification algorithm, the selected channels would be changed. In this study, a new method named multi-class common spatial pattern (MCCSP) was proposed for EMG selection in EMG pattern-recognition-based movement classification. Since MCCSP is independent on specific EMG features and classification algorithms, it would be more convenient for channel selection in developing an EMG control system than the exiting methods.MethodsThe performance of the proposed MCCSP method in selecting some optimal EMG channels (designated as a subset) was assessed with high-density EMG recordings from twelve mildly-impaired traumatic brain injury (TBI) patients. With the MCCSP method, a subset of EMG channels was selected and then used for motion classification with pattern recognition technique. In order to justify the performance of the MCCSP method against different electrode configurations, features and classification algorithms, two electrode configurations (unipolar and bipolar) as well as two EMG feature sets and two types of pattern recognition classifiers were considered in the study, respectively. And the performance of the proposed MCCSP method was compared with that of two exiting channel selection methods (SFS and FMS) in EMG control system.ResultsThe results showed that in comparison with the previously used SFS and FMS methods, the newly proposed MCCSP method had better motion classification performance. Moreover, a fixed combination of the selected EMG channels was obtained when using MCCSP.ConclusionsThe proposed MCCSP method would be a practicable means in channel selection and would facilitate the design of practical myoelectric control systems in the active rehabilitation of mildly-impaired TBI patients and in other rehabilitation applications such as the multifunctional myoelectric prostheses for limb amputees.

Previous studies of electromyographic (EMG) pattern recognition for neural prosthesis control mainly focused on the estimation of offline classification accuracy. Factors that may affect the performance in operating prosthesis in practice were rarely considered. In the preliminary study we investigated effects of the variation of limb positions on classification performance. Eight channels of myoelectric signals and a LDA classifier were used to identify seven classes of forearm movements in five transradial amputees. Our pilot results showed that the classification error of inter-position was about 4 times more than that of single position across the five transradial amputees (p<0.02), indicating the significant effect of limb position variation on classification performance. We further attempted to reduce the limb position effect by training the classifier with EMG from multiple EMG recording positions, and the classification performance was compared with that of single position training. It was found multiple positions training method could decrease the classification error as more positions involved, which reached its minimum value 11.25% when all five positions were included.

The study was approved by the ethics committee of Institutional Review Board of Shenzhen Institutes of Advanced Technology, and the reference number is SIAT-IRB-150515-H0077.

A couple of studies have been conducted with able-bodied subjects and/or arm amputees to investigate the impact of arm position changes in the practical use of a multifunctional myoelectric prosthesis. The classification accuracy calculated offline from electromyography (EMG) recordings was used as a performance metric in these studies, which is not a true measure of real-time control performance. In this study, the influence of arm position changes on the real-time performance of EMG pattern recognition (EMG-PR) control was quantitatively evaluated with four real-time metrics including motion response time, motion completion time, motion completion rate, and dynamic efficiency. Ten able-bodied subjects participated in the study and a cascade classifier built with both EMG and mechanomyogram (MMG) recordings was proposed to reduce the impact of arm position variation. The pilot results showed that arm position changes would substantially affect the real-time performance of EMG pattern-recognition based prosthesis control. Using a cascade classifier could significantly increase the average real-time completion rate (p-value<0.01). This suggests that the proposed cascade classifier may have potential to reduce the influence of arm position variation on the real-time control performance of a prosthesis.