Lizhi Pan

Continuous estimation of finger joint angles under different static wrist motions from surface EMG signals

Abstract In this paper, a solution is proposed to predict the finger joint angle using electromyography (EMG) towards application for partial-hand amputees with functional wrist. In the experimental paradigm, the subject was instructed to continuously move one finger (middle finger for able-bodied subjects and index finger for partial-hand amputees) up to the maximum angle of flexion and extension while the wrist was conducting seven different wrist motions. A switching regime, including one linear discriminant analysis (LDA) classifier and fourteen state-space models, was proposed to continuously decode finger joint angles. LDA classifier was used to recognize which static wrist motion that the subject was conducting and choose the corresponding two state-space models for decoding joint angles of the finger with two degrees of freedom (DOFs). The average classification error rate (CER) was 6.18%, demonstrating that these seven static wrist motions along with the continuous movement of the finger could be classified. To improve the classification performance, a preprocessing method, class-wise stationary subspace analysis (cwSSA), was firstly adopted to extract the stationary components from original EMG data. Consequently, the average CER was reduced by 1.82% ( p R 2 ) of the two joint angles of the finger across seven static wrist motions achieved 0.843. This result shows that the fingers joint angles can be continuously estimated well while the wrist was conducting different static motions simultaneously. The average accuracy of seven static wrist motions with and without cwSSA and the average estimation performance of the two joint angles of the finger prove that the proposed switching regime is effective for continuous estimation of the finger joint angles under different static wrist motions from EMG.

Improving robustness against electrode shift of high density EMG for myoelectric control through common spatial patterns.

BackgroundMost prosthetic myoelectric control studies have concentrated on low density (less than 16 electrodes, LD) electromyography (EMG) signals, due to its better clinical applicability and low computation complexity compared with high density (more than 16 electrodes, HD) EMG signals. Since HD EMG electrodes have been developed more conveniently to wear with respect to the previous versions recently, HD EMG signals become an alternative for myoelectric prostheses. The electrode shift, which may occur during repositioning or donning/doffing of the prosthetic socket, is one of the main reasons for degradation in classification accuracy (CA).MethodsHD EMG signals acquired from the forearm of the subjects were used for pattern recognition-based myoelectric control in this study. Multiclass common spatial patterns (CSP) with two types of schemes, namely one versus one (CSP-OvO) and one versus rest (CSP-OvR), were used for feature extraction to improve the robustness against electrode shift for myoelectric control. Shift transversal (ST1 and ST2) and longitudinal (SL1 and SL2) to the direction of the muscle fibers were taken into consideration. We tested nine intact-limb subjects for eleven hand and wrist motions. The CSP features (CSP-OvO and CSP-OvR) were compared with three commonly used features, namely time-domain (TD) features, time-domain autoregressive (TDAR) features and variogram (Variog) features.ResultsCompared with the TD features, the CSP features significantly improved the CA over 10 % in all shift configurations (ST1, ST2, SL1 and SL2). Compared with the TDAR features, a. the CSP-OvO feature significantly improved the average CA over 5 % in all shift configurations; b. the CSP-OvR feature significantly improved the average CA in shift configurations ST1, SL1 and SL2. Compared with the Variog features, the CSP features significantly improved the average CA in longitudinal shift configurations (SL1 and SL2).ConclusionThe results demonstrated that the CSP features significantly improved the robustness against electrode shift for myoelectric control with respect to the commonly used features.

Improving Myoelectric Control for Amputees through Transcranial Direct Current Stimulation

Most prosthetic myoelectric control studies have shown good performance for unimpaired subjects. However, performance is generally unacceptable for amputees. The primary problem is the poor quality of electromyography (EMG) signals of amputees compared with healthy individuals. To improve clinical performance of myoelectric control, this study explored transcranial direct current stimulation (tDCS) to modulate brain activity and enhance EMG quality. We tested six unilateral transradial amputees by applying active and sham anodal tDCS separately on two different days. Surface EMG signals were acquired from the affected and intact sides for 11 hand and wrist motions in the pre-tDCS and post-tDCS sessions. Autoregression coefficients and linear discriminant analysis classifiers were used to process the EMG data for pattern recognition of the 11 motions. For the affected side, active anodal tDCS significantly reduced the average classification error rate (CER) by 10.1%, while sham tDCS had no such effect. For the intact side, the average CER did not change on the day of sham tDCS but increased on the day of active tDCS. These results demonstrated that tDCS could modulate brain function and improve EMG-based classification performance for amputees. It has great potential in dramatically reducing the length of learning process of amputees for effectively using myoelectrically controlled multifunctional prostheses.

A linear model for simultaneously and proportionally estimating wrist kinematics from emg during mirrored bilateral movements

This paper presents a linear model for simultaneous and proportional estimation of the two degree-of-freedoms (DOFs) wrist angle positions with surface electromyography (EMG). A 5th order state-space model was used to estimate wrist kinematics from 4-channel surface EMG signals of the contralateral forearm during mirrored bilateral movements without motion constraints. The EMG signal from each of the three limbed normal subjects was collected along with each angle position in two DOFs from both of the arms, with motion parameters tested including the radial/ulnar deviation and flexion/extension of the wrist. The estimation performance was in the range 0.787-0.885 (R2 index) for the two DOFs in three limbed normal subjects. The results show that wrist kinematics can be estimated in 2 DOFs by state-space models with relative high accuracy compared with the results reported previously. The method proposed, as requiring only kinematics measured from the contralateral wrist, is potentially available for a unilateral amputee in simultaneous and proportional control of DOFs in powered upper limb prostheses.

Transcranial direct current stimulation versus user training on improving online myoelectric control for amputees

OBJECTIVE Transcranial direct current stimulation (tDCS) and user training (UT) are two types of methods to improve myoelectric control performance for amputees. In this study, we compared the independent effect between tDCS and UT, and investigated the combined effect of tDCS and UT. APPROACH An online paradigm of simultaneous and proportional control (SPC) based on electromyography (EMG) was adopted. The proposed experiments were conducted on six naïve unilateral trans-radial amputees. The subjects each received three types of 20 min interventions: active tDCS with motor training (tDCS  +  UT), active tDCS with quiet sitting (tDCS), and sham tDCS with motor training (UT). The interventions were applied at one week intervals in a randomized order. The subjects performed online control of a feedback arrow with two degrees of freedom (DoFs) to accomplish target reaching motor tasks in pre-sessions and post-sessions. We compared the performance, measured by completion rate, completion time, and efficiency coefficient, between pre-sessions and post-sessions. MAIN RESULTS The results showed that the intervention tDCS  +  UT and tDCS significantly improved the online SPC performance (i.e. improved the completion rate; reduced the completion time; and improved the efficiency coefficient), while intervention UT did not significantly change the performance. The results also showed that the online SPC performance after intervention tDCS  +  UT and tDCS was not significantly different, but both were significantly better than that after intervention UT. SIGNIFICANCE tDCS could be an effective intervention to improve the online SPC performance in a short time.

Can transcranial direct current stimulation enhance performance of myoelectric control for multifunctional prosthesis

Pattern recognition based myoelectric control has been studied by many researchers. However, the classification accuracy was pretty low for amputees towards multifunctional prosthesis control in practice. In this work, a novel method of transcranial direct current stimulation (tDCS) which can modulate brain activity was used to enhance performance for myoelectric prosthesis control. The pilot study was conducted on three able-bodied subjects and one transradial amputee. Surface electromyography (EMG) signals were acquired from both arms when performing eleven hand and wrist motions in pre-tDCS and post-tDCS sessions. Time domain (TD) features and linear discriminant analysis (LDA) classifier were adopted to process EMG. For the non-dominant hand of the healthy subjects, active anodal tDCS of the contralateral primary motor cortex was able to significantly improve average classification accuracy by 3.82% (p <; 0.05), while sham tDCS could not have such effect (p > 0.05). For amputated (phantom) hand of the amputee, active anodal tDCS was able to significantly improve average classification accuracy by 12.56%, while sham tDCS could not have such effect. For the dominant hand and intact hand, the average classification accuracies were stable and not significantly improved using either active tDCS or sham tDCS. The results show that tDCS is a powerful noninvasive method to modulate brain function and enhance EMG classification performance especially for the amputated hand towards multifunctional prosthesis control. The method proposed has a huge potential to promote EMG pattern recognition based control scheme to clinical application.

Rate-dependent hysteresis in the EMG-force relationship: A new discovery in EMG-force relationship

In this study, we analyzed the existence of rate-dependent hysteresis in the electromyography (EMG)-force relationship. Eight able-bodied subjects participated in the experiment. Surface EMG signals were acquired from flexor pollicis longus muscle from 0% to 100% maximum voluntary contraction (MVC). The subject was asked to gradually increase grasping force from 0% to 100% MVC and decrease grasping force from 100% to 0% MVC at five different frequencies (1.5, 1, 0.5, 0.25 and 0.125 Hz). Mean absolute value (MAV) was chosen to represent the EMG signals and force signals. In order to compare differences in force between contraction and relaxation periods to EMG activity among different frequency conditions, a hysteresis index (HI), defined as an area inside the hysteresis cycle, was adopted. The results showed that all mean values of HI in different frequency conditions were larger than 0, which proved that hysteresis cycles existed in all frequency conditions. The results also showed that the HI values in different frequency conditions were significantly different from each other (p <; 0.005), which proved hysteresis effects in EMG-force relationship were rate-dependent. The rate-dependent hysteresis in EMG-force relationship has a huge potential to improve the estimation performance of grasping force from EMG.

Residuals of autoregressive model providing additional information for feature extraction of pattern recognition-based myoelectric control

Myoelectric control based on pattern recognition has been studied for several decades. Autoregressive (AR) features are one of the mostly used feature extraction methods among myoelectric control studies. Almost all previous studies only used the AR coefficients without the residuals of AR model for classification. However, the residuals of AR model contain important amplitude information of the electromyography (EMG) signals. In this study, we added the residuals to the AR features (AR+re) and compared its performance with the classical sixth-order AR coefficients. We tested six unilateral transradial amputees and eight able-bodied subjects for eleven hand and wrist motions. The classification accuracy (CA) of the intact side for amputee subjects and the right hand for able-bodied subjects showed that the CA of AR+re features was slightly but significantly higher than that of classical AR features (p = 0.009), which meant that residuals could provide additional information to classical AR features for classification. Interestingly, the CA of the affected side for amputee subjects showed that there was no significant difference between the CA of AR+re features and classical AR features (p > 0.05). We attributed this to the fact that the amputee subjects could not use their affected side to produce consistent EMG patterns as their intact side or the dominant hand of the able-bodied subjects. Since the residuals were already available when the AR coefficients were computed, the results of this study suggested adding the residuals to classical AR features to potentially improve the performance of pattern recognition-based myoelectric control.

A structurally decoupled mechanism for measuring wrist torque in three degrees of freedom.

The wrist joint is a critical part of the human body for movement. Measuring the torque of the wrist with three degrees of freedom (DOFs) is important in some fields, including rehabilitation, biomechanics, ergonomics, and human-machine interfacing. However, the particular structure of the wrist joint makes it difficult to measure the torque in all three directions simultaneously. This work develops a structurally decoupled instrument for measuring and improving the measurement accuracy of 3-DOF wrist torque during isometric contraction. Three single-axis torque sensors were embedded in a customized mechanical structure. The dimensions and components of the instrument were designed based on requirement of manufacturability. A prototype of the instrument was machined, assembled, integrated, and tested. The results show that the structurally decoupled mechanism is feasible for acquiring wrist torque data in three directions either independently or simultaneously. As a case study, we use the device to measure wrist torques concurrently with electromyography signal acquisition in preparation for simultaneous and proportional myoelectric control of prostheses.