Guanglin Li

Quantifying Pattern Recognition—Based Myoelectric Control of Multifunctional Transradial Prostheses

We evaluated real-time myoelectric pattern recognition control of a virtual arm by transradial amputees. Five unilateral patients performed 10 wrist and hand movements using their amputated and intact arms. In order to demonstrate the value of information from intrinsic hand muscles, this data was included in EMG recordings from the intact arm. With both arms, motions were selected in approximately 0.2 s on average, and completed in less than 1.25 s. Approximately 99% of wrist movements were completed using either arm; however, the completion rate of hand movements was significantly lower for the amputated arm (53.9% ± 14.2%) than for the intact arm ( 69.4% ± 13.1%). For the amputated arm, average classification accuracy for only 6 movements-including a single hand grasp-was 93.1% ± 4.1%, compared to 84.4% ± 7.2% for all 10 movements. Use of 6 optimally-placed electrodes only reduced this accuracy to 91.5% ± 4.9%. These results suggest that muscles in the residual forearm produce sufficient myoelectric information for real-time wrist control, but not for performing multiple hand grasps. The outcomes of this study could aid the development of a practical multifunctional myoelectric prosthesis for transradial amputees, and suggest that increased EMG information-such as made available through targeted muscle reinnervation-could improve control of these prostheses .

Principal Components Analysis Preprocessing for Improved Classification Accuracies in Pattern-Recognition-Based Myoelectric Control

Information extracted from multiple channels of the surface myoelectric signal (MES) recording sites can be used as inputs to control systems for powered upper limb prostheses. For small, closely spaced muscles, such as the muscles in the forearm, the detected MES often contains contributions from more than one muscle, the contribution from each specific muscle being modified by the dispersive propagation through the volume conductor between the muscle and the detection points. In this paper, the measured raw MES signals are rotated by class-specific principal component matrices to spatially decorrelate the measured data prior to feature extraction. This ldquotunesrdquo the data to allow a pattern recognition classifier to better discriminate the test motions. This processing technique was used to significantly (p<0.01) reduce pattern recognition classification error for both intact limbed and transradial amputee subjects.

An Analysis of EMG Electrode Configuration for Targeted Muscle Reinnervation Based Neural Machine Interface

Targeted muscle reinnervation (TMR) is a novel neural machine interface for improved myoelectric prosthesis control. Previous high-density (HD) surface electromyography (EMG) studies have indicated that tremendous neural control information can be extracted from the reinnervated muscles by EMG pattern recognition (PR). However, using a large number of EMG electrodes hinders clinical application of the TMR technique. This study investigated a reduced number of electrodes and the placement required to extract sufficient neural control information for accurate identification of user movement intents. An electrode selection algorithm was applied to the HD EMG recordings from each of four TMR amputee subjects. The results show that when using only 12 selected bipolar electrodes the average accuracy over subjects for classifying 16 movement intents was 93.0 (plusmn3.3)%, just 1.2% lower than when using the entire HD electrode complement. The locations of selected electrodes were consistent with the anatomical reinnervation sites. Additionally, a practical protocol for clinical electrode placement was developed, which does not rely on complex HD EMG experiment and analysis while maintaining a classification accuracy of 88.7plusmn4.5%. These outcomes provide important guidelines for practical electrode placement that can promote future clinical application of TMR and EMG PR in the control of multifunctional prostheses.

Localization of the site of origin of cardiac activation by means of a heart-model-based electrocardiographic imaging approach

The authors have developed a new approach to solve the inverse problem of electrocardiography in terms of heart model parameters. The inverse solution of the electrocardiogram (ECG) inverse problem is defined, in the present study, as the parameters of the heart model, which are closely related to the physiological and pathophysiological status of the heart, and is estimated by using an optimization system of heart model parameters, instead of solving the matrix equation relating the body surface ECGs and equivalent cardiac sources. An artificial neural network based preliminary diagnosis system has been developed to limit the searching space of the optimization algorithm and to initialize the model parameters in the computer heart model. The optimal heart model parameters were obtained by minimizing the objective functions, as functions of the observed and model-generated body surface ECGs. The authors have tested the feasibility of the newly developed technique in localizing the site of origin of cardiac activation using a pace mapping protocol. The present computer simulation results show that, the present approach for localization of the site of origin of ventricular activation achieved an averaged localization error of about 3 mm [for 5-/spl mu/V Gaussian white noise (GWN)] and 4 mm (for 10-/spl mu/V GWN), with standard deviation of the localization errors of being about 1.5 mm. The present simulation study suggests that this newly developed approach provides a robust inverse solution, circumventing the difficulties of the ECG inverse problem, and may become an important alternative to other ECG inverse solutions.

High-resolution EEG: a new realistic geometry spline Laplacian estimation technique

BACKGROUND A new realistic geometry (RG) spline Laplacian estimation technique has been developed for high-resolution EEG imaging. METHODS Estimation of the parameters associated with the spline Laplacian is formulated by seeking the general inverse of a transfer matrix. The number of spline parameters, which need to be determined through regularization, is reduced to one in the present approach, thus enabling easy implementation of the RG spline Laplacian estimator. RESULTS Computer simulation studies have been conducted to test the feasibility of the new approach in a 3-concentric-sphere head model. The new technique has also been applied to human visual evoked potential data with a RG head model. CONCLUSIONS The present numerical and experimental results demonstrate the feasibility of the new approach and indicate that the RG spline Laplacian can be estimated easily from the surface potentials and the scalp geometry.

Toward attenuating the impact of arm positions on electromyography pattern-recognition based motion classification in transradial amputees

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.

Noninvasive three-dimensional activation time imaging of ventricular excitation by means of a heart-excitation model

We propose a new method for imaging activation time within three-dimensional (3D) myocardium by means of a heart-excitation model. The activation time is estimated from body surface electrocardiograms by minimizing multiple objective functions of the measured body surface potential maps (BSPMs) and the heart-model-generated BSPMs. Computer simulation studies have been conducted to evaluate the proposed 3D myocardial activation time imaging approach. Single-site pacing at 24 sites throughout the ventricles, as well as dual-site pacing at 12 pairs of sites in the vicinity of atrioventricular ring, was performed. The present simulation results show that the average correlation coefficient (CC) and relative error (RE) for single-site pacing were 0.9992+/-0.0008/0.9989+/-0.0008 and 0.05+/-0.02/0.07+/-0.03, respectively, when 5 microV/10 microV Gaussian white noise (GWN) was added to the body surface potentials. The average CC and RE for dual-site pacing were 0.9975+/-0.0037 and 0.08+/-0.04, respectively, when 10 microV GWN was added to the body surface potentials. The present simulation results suggest the feasibility of noninvasive estimation of activation time throughout the ventricles from body surface potential measurement, and suggest that the proposed method may become an important alternative in imaging cardiac electrical activity noninvasively.

An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction

This study proposed a hybrid decision support method (ANN and Fuzzy_AHP) for heart failure prediction.The performance of the proposed method was examined using three performance metrics.From the evaluations results, the proposed method performed better than the conventional ANN approachThe proposed method would provide improved and realistic result for efficient therapy administration. Heart failure (HF) has been considered as one of the deadliest human diseases worldwide and the accurate prediction of HF risks would be vital for HF prevention and treatment. To predict HF risks, decision support systems based on artificial neural networks (ANN) have been widely proposed in previous studies. Generally, these existing ANN-based systems usually assumed that HF attributes have equal risk contribution to the HF diagnosis. However, several previous investigations have shown that the risk contributions of the attributes would be different. Thus the equal risk assumption concept associated with existing ANN methods would not properly reflect the diagnosis status of HF patients. In this study, the commonly used 13 HF attributes were considered and their contributions were determined by an experienced cardiac clinician. And Fuzzy analytic hierarchy process (Fuzzy_AHP) technique was used to compute the global weights for the attributes based on their individual contribution. Then the global weights that represent the contributions of the attributes were applied to train an ANN classifier for the prediction of HF risks in patients. The performance of the newly proposed decision support system based on the integration of ANN and Fuzzy_AHP methods was evaluated by using online clinical dataset of 297 HF patients and compared with that of the conventional ANN method. Our result shows that the proposed method could achieve an average prediction accuracy of 91.10%, which is 4.40% higher in comparison to that of the conventional ANN method. In addition, the newly proposed method also had better performance than seven previous methods that reported prediction accuracies in the range of 57.85-89.01%. The improvement of the HF risk prediction in the current study might be due to both the various contributions of the HF attributes and the proposed hybrid method. These findings suggest that the proposed method could be used to accurately predict HF risks in the clinic.

Fuzzy Approximation-Based Adaptive Backstepping Control of an Exoskeleton for Human Upper Limbs

This paper presents fuzzy approximation-based adaptive backstepping control of an exoskeleton for human upper limbs to provide forearm movement assistance so that a human forearm can track any continuous desired trajectory (or constant setpoint) in the presence of parametric/functional uncertainties, unmodeled dynamics, actuator dynamics, and/or disturbances from environments. Given the desired trajectories of human forearm positions, in the developed control, adaptive fuzzy approximators are used to estimate the dynamical uncertainties of the human-robot system, and an iterative learning scheme is utilized to compensate for unknown time-varying periodic disturbances. With the synthesis of the backstepping, iterative learning, and Lyapunov function approaches, the developed controller does not require exact knowledge of the exoskeleton model, and the close-loop system can be proven to be semiglobally uniformly bounded. Three comparison experiments are conducted to illustrate the effectiveness of the proposed control scheme by tracking periodic/repeated trajectories.

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