Xiangxin Li

Pattern recognition of electromyography signals based on novel time domain features for amputees' limb motion classification☆

Abstract Feature extraction is essential in Electromyography pattern recognition (EMG-PR) based prostheses control method. Time-domain features have been shown to have good performance in upper limb movement classification. However, the performance of EMG-PR prostheses driven by the existing time-domain features is still unsatisfactory. Hence, this study proposed three new time-domain features to improve the performance of EMG-PR based strategy in arm movement classification. EMG signals were recorded from the residual arms of eight amputees while performing different upper limb movements. Then, the newly proposed features were extracted and used to classify their limb movements. Experimental results showed that the proposed features could achieved an average classification accuracy of 92.00% ± 3.11% which was 6.49% higher than that of the commonly used time-domain features (p

A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees

BackgroundMost of the modern motorized prostheses are controlled with the surface electromyography (sEMG) recorded on the residual muscles of amputated limbs. However, the residual muscles are usually limited, especially after above-elbow amputations, which would not provide enough sEMG for the control of prostheses with multiple degrees of freedom. Signal fusion is a possible approach to solve the problem of insufficient control commands, where some non-EMG signals are combined with sEMG signals to provide sufficient information for motion intension decoding. In this study, a motion-classification method that combines sEMG and electroencephalography (EEG) signals were proposed and investigated, in order to improve the control performance of upper-limb prostheses.MethodsFour transhumeral amputees without any form of neurological disease were recruited in the experiments. Five motion classes including hand-open, hand-close, wrist-pronation, wrist-supination, and no-movement were specified. During the motion performances, sEMG and EEG signals were simultaneously acquired from the skin surface and scalp of the amputees, respectively. The two types of signals were independently preprocessed and then combined as a parallel control input. Four time-domain features were extracted and fed into a classifier trained by the Linear Discriminant Analysis (LDA) algorithm for motion recognition. In addition, channel selections were performed by using the Sequential Forward Selection (SFS) algorithm to optimize the performance of the proposed method.ResultsThe classification performance achieved by the fusion of sEMG and EEG signals was significantly better than that obtained by single signal source of either sEMG or EEG. An increment of more than 14% in classification accuracy was achieved when using a combination of 32-channel sEMG and 64-channel EEG. Furthermore, based on the SFS algorithm, two optimized electrode arrangements (10-channel sEMG + 10-channel EEG, 10-channel sEMG + 20-channel EEG) were obtained with classification accuracies of 84.2 and 87.0%, respectively, which were about 7.2 and 10% higher than the accuracy by using only 32-channel sEMG input.ConclusionsThis study demonstrated the feasibility of fusing sEMG and EEG signals towards improving motion classification accuracy for above-elbow amputees, which might enhance the control performances of multifunctional myoelectric prostheses in clinical application.Trial registrationThe 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.

Towards reducing the impacts of unwanted movements on identification of motion intentions

Surface electromyogram (sEMG) has been extensively used as a control signal in prosthesis devices. However, it is still a great challenge to make multifunctional myoelectric prostheses clinically available due to a number of critical issues associated with existing EMG based control strategy. One such issue would be the effect of unwanted movements (UMs) that are inadvertently done by users on the performance of movement classification in EMG pattern recognition based algorithms. Since UMs are not considered in training a classifier, they would decay the performance of a trained classifier in identifying the target movements (TMs), which would cause some undesired actions in control of multifunctional prostheses. In this study, the impact of UMs was systemically investigated in both able-bodied subjects and transradial amputees. Our results showed that the UMs would be unevenly classified into all classes of the TMs. To reduce the impact of the UMs on the performance of a classifier, a new training strategy that would categorize all possible UMs into a new movement class was proposed and a metric called Reject Ratio that is a measure of how many UMs is rejected by a trained classifier was adopted. The results showed that the average Reject Ratio across all the participants was greater than 91%, meanwhile the average classification accuracy of TMs was above 99% when UMs occurred. This suggests that the proposed training strategy could greatly reduce the impact of UMs on the performance of the trained classifier in identifying the TMs and may enhance the robustness of myoelectric control in clinical applications.

Effects of non-training movements on the performance of motion classification in electromyography pattern recognition

In electromyography pattern-recognition-based control of a multifunctional prosthesis, it would be inevitable for the users to unintentionally perform some classes of movements that are excluded from the training motion classes of a classifier, which might decay the performance of a trained classifier. It remains unknown how these untrained movements, designated as non-target movements (NTMs) in the study, would affect the performance of a trained classifier in the control of multifunctional prostheses. The goal of the current study was to evaluate the effects of NTMs on the performance of movement classification. Five classes of target movements (TMs) and four classes of NTMs were considered in this pilot study. A classifier based on a linear discriminant analysis (LDA) was trained with the electromyography (EMG) signals from the five TMs and the effects of the four NTMs were examined by feeding the EMG signals of the four NTMs to the trained classifier. Our results showed that these NTMs were classified into one or more classes of the TMs, which would cause the unexpected movements of prostheses. A method to reduce the effects of NTMs has been proposed in the study and our results showed that the averaged classification accuracies of the corrected classifiers were above 99% for the healthy subjects.

Fabrication, Structure Characterization, and Performance Testing of Piezoelectret-Film Sensors for Recording Body Motion

During muscle contractions, radial-force distributions are generated on muscle surfaces due to muscle-volume changes, from which the corresponding body motions can be recorded by means of so-called force myography (FMG). Piezo-or ferroelectrets are flexible piezoelectric materials with attractive materials and sensing properties. In addition to several other applications, they are suitable for detecting force variations by means of wearable devices. In this paper, we prepared piezoelectrets from cellular polypropylene films by optimizing the fabrication procedures, and developed an FMG-recording system based on piezoelectret sensors. Different hand and wrist movements were successfully detected on able-bodied subjects with the FMG system. The FMG patterns were evaluated and identified by means of linear discriminant analysis and artificial neural network algorithms, and average motion-classification accuracies of 96.1% and 94.8%, respectively, were obtained. This paper demonstrates the feasibility of using piezoelectret-film sensors for FMG and may thus lead to alternative methods for detecting body motion and to related applications, e.g., in biomedical engineering or structural-health monitoring.

A new approach to mitigate the effect of force variation on pattern recognition for myoelectric control

In myoelectric prosthetic control, the motion classification performance would be decayed if an electromyography (EMG) pattern to be recognized differs significantly from the one used for classifier training. Generally, the training signals are acquired when a subject performs motions with a proper force. In practical use of a myoelectric prosthesis, however, the variation of force levels to do a motion would be inevitable, which will cause a change of EMG patterns. In this study, we proposed a Parallel Classification Strategy consisting of three parallel classifiers created at low, medium, and high force levels, respectively, and designed a regulation to categorize the input signals into corresponding classifiers for pattern recognition. The pilot experimental results demonstrated that the proposed method could enhance the classification accuracy at different force levels with an average classification rate of 98.8%, which was higher than the current method (91.9%). Thus, the proposed method might improve the control performance for present myoelectric prostheses.In myoelectric prosthetic control, the motion classification performance would be decayed if an electromyography (EMG) pattern to be recognized differs significantly from the one used for classifier training. Generally, the training signals are acquired when a subject performs motions with a proper force. In practical use of a myoelectric prosthesis, however, the variation of force levels to do a motion would be inevitable, which will cause a change of EMG patterns. In this study, we proposed a Parallel Classification Strategy consisting of three parallel classifiers created at low, medium, and high force levels, respectively, and designed a regulation to categorize the input signals into corresponding classifiers for pattern recognition. The pilot experimental results demonstrated that the proposed method could enhance the classification accuracy at different force levels with an average classification rate of 98.8%, which was higher than the current method (91.9%). Thus, the proposed method might improve the control performance for present myoelectric prostheses.

FMG-based body motion registration using piezoelectret sensors

Body motion registration can provide plenty of muscle activity information of human beings, which is applicable in the control of human-computer interfaces or real devices. Forcemyography (FMG) is a method to register real-time body motions by measuring the radially directed force distributions that are generated by muscle contractions. In this work, we recorded FMG maps by using a novel type of sensor, the polymer-based piezoelectrets. With five piezoelectret sensor units attached on the surface of thigh muscles, four basic lower-limb motions, leg-raising, leg-dropping, knee-extension, and knee-flexion, were properly captured on four able-bodied subjects. Motion classification accuracies of 92.9%, 84.8%, and 88.1% were obtained by using different recognition algorithms of KNN, LDA, and ANN, respectively. The pilot experimental results demonstrated the feasibility of FMG recording by using piezoelectret sensors, which may provide an alternative method for body motion registration.

Improving the Robustness of Electromyogram-Pattern Recognition for Prosthetic Control by a Postprocessing Strategy

Electromyogram (EMG) contains rich information for motion decoding. As one of its major applications, EMG-pattern recognition (PR)-based control of prostheses has been proposed and investigated in the field of rehabilitation robotics for decades. These prostheses can offer a higher level of dexterity compared to the commercially available ones. However, limited progress has been made toward clinical application of EMG-PR-based prostheses, due to their unsatisfactory robustness against various interferences during daily use. These interferences may lead to misclassifications of motion intentions, which damage the control performance of EMG-PR-based prostheses. A number of studies have applied methods that undergo a postprocessing stage to determine the current motion outputs, based on previous outputs or other information, which have proved effective in reducing erroneous outputs. In this study, we proposed a postprocessing strategy that locks the outputs during the constant contraction to block out occasional misclassifications, upon detecting the motion onset using a threshold. The strategy was investigated using three different motion onset detectors, namely mean absolute value, Teager–Kaiser energy operator, or mechanomyogram (MMG). Our results indicate that the proposed strategy could suppress erroneous outputs, during rest and constant contractions in particular. In addition, with MMG as the motion onset detector, the strategy was found to produce the most significant improvement in the performance, reducing the total errors up to around 50% (from 22.9 to 11.5%) in comparison to the original classification output in the online test, and it is the most robust against threshold value changes. We speculate that motion onset detectors that are both smooth and responsive would further enhance the efficacy of the proposed postprocessing strategy, which would facilitate the clinical application of EMG-PR-based prosthetic control.