Myoelectric Pattern Recognition Research Articles

Botulinum toxin treatment may improve myoelectric pattern recognition in robot-assisted stroke rehabilitation.

Botulinum toxin treatment may improve myoelectric pattern recognition in robot-assisted stroke rehabilitation.

Robust myoelectric pattern recognition methods for reducing users' calibration burden: challenges and future.

Myoelectric pattern recognition (MPR) has evolved into a sophisticated technology widely employed in controlling myoelectric interface (MI) devices like prosthetic and orthotic robots. Current MIs not only enable multi-degree-of-freedom control of prosthetic limbs but also demonstrate substantial potential in consumer electronics. However, the non-stationary random characteristics of myoelectric signals poses challenges, leading to performance degradation in practical scenarios such as electrode shifting and switching new users. Conventional MIs often necessitate meticulous calibration, imposing a significant burden on users. To address user frustration during the calibration process, researchers have focused on identifying MPR methods that alleviate this burden. This article categorizes common scenarios that incur calibration burdens as based on data distribution shift and based on dynamic data categories. Then further investigated and summarized the popular robust MPR algorithms used to reduce the user's calibration burden. We categorize these algorithms as based on data manipulate, feature manipulation and, model structure. And describes the scenarios to which each method is applicable and the conditions required for calibration. Finally, this review is concluded with the advantages of robust MPR and the remaining challenges and future opportunities.

Novel Wearable Device for Mindful Sensorimotor Training: Integrating Motor Decoding and Somatosensory Stimulation for Neurorehabilitation.

Sensorimotor impairment is a prevalent condition requiring effective rehabilitation strategies. This study introduces a novel wearable device for Mindful Sensorimotor Training (MiSMT) designed for sensory and motor rehabilitation. Our MiSMT device combines motor training using myoelectric pattern recognition along sensory training using two tactile displays. This device offers a comprehensive solution, integrating electromyography and haptic feedback, lacking in existing devices. The device features eight electromyography channels, a rechargeable battery, and wireless Bluetooth or Wi-Fi connectivity for seamless communication with a computer or mobile device. Its flexible material allows for adaptability to various body parts, ensuring ease of use in diverse patients. The two tactile displays, with 16 electromagnetic actuators each, provide touch and vibration sensations up to 250 Hz. In this proof-of-concept study, we show improved two-point discrimination after 5 training sessions in participants with intact limbs (p=0.047). We also demonstrated successful acquisition, processing, and decoding of myoelectric signals in offline and online evaluations. In conclusion, the MiSMT device presents a promising tool for sensorimotor rehabilitation by combining motor execution and sensory training benefits. Further studies are required to assess its effectiveness in individuals with sensorimotor impairments. Integrating mindful sensory and motor training with innovative technology can enhance rehabilitation outcomes and improve the quality of life for those with sensorimotor impairments.

Implications of EMG channel count: enhancing pattern recognition online prosthetic testing.

Myoelectric pattern recognition systems have shown promising control of upper limb powered prostheses and are now commercially available. These pattern recognition systems typically record from up to 8 muscle sites, whereas other control systems use two-site control. While previous offline studies have shown 8 or fewer sites to be optimal, real-time control was not evaluated. Six individuals with no limb absence and four individuals with a transradial amputation controlled a virtual upper limb prosthesis using pattern recognition control with 8 and 16 channels of EMG. Additionally, two of the individuals with a transradial amputation performed the Assessment for Capacity of Myoelectric Control (ACMC) with a multi-articulating hand and wrist prosthesis with the same channel count conditions. Users had significant improvements in control when using 16 compared to 8 EMG channels including decreased classification error (p = 0.006), decreased completion time (p = 0.019), and increased path efficiency (p = 0.013) when controlling a virtual prosthesis. ACMC scores increased by more than three times the minimal detectable change from the 8 to the 16-channel condition. The results of this study indicate that increasing EMG channel count beyond the clinical standard of 8 channels can benefit myoelectric pattern recognition users.

Short-term hand gestures recognition based on electromyography signals

<p>Electromyography pattern recognition to predict limb movements can<br />significantly enhance the control of the prosthesis. However, this technique<br />has not yet been widely used in clinical practice. Improvements in the<br />myoelectric pattern recognition (MPR) system can improve the functionality<br />of the prosthesis. This study proposes new sets of time domain features to<br />enhance the MPR control system. Three groups of features are evaluated, time<br />domain with auto regression (TD-AR), frequency domain (FD), and timefrequency domain (TFD). The electromyography signals (EMG) are obtained from the Ninapro database-5 (DB5), a publicly available dataset for hand prosthetics. The long-term signals of DB5 are divided into short-term signals to perform short-term signals recognition. The three feature sets are extracted from the short-term signals. The results showed that the performance of the proposed TD-AR features outperformed that of the FD and TFD feature sets. The TD-AR-based discrimination performance of 40 gestures achieved a precision of 88.8% and a sensitivity of 82.6%. The integration of short-term identification with reliable features can improve classification accuracy even for a large number of gestures. A comparison with the latest works shows the reliability of the proposed work.</p>

909 Current Challenges and Future Prospects of Osseointegration Limb Reconstruction for Amputees

Abstract Aim The field of osseointegration has existed for almost 30 years and now appears to be on the verge of greater acceptance and widespread implementation. We reviewed the current challenges and future prospects of osseointegration limb reconstruction. Method A scoping review of the literature was conducted by researchers with an interest in Osseoinegration and key papers were identified and summarized. Results The 3 biggest challenges identified included Risk of Infection, High Cost and lack of outcome measures. The solutions identified for the advances in Infection prevention and control included; Smart Implant Coating, Mechanical inactivation of Staphylococcus aureus and Pseudomonas aeruginosa, Biofilm erradication, Simultaneous Monitoring of Loosening and Temperature in Orthopaedic Implants, Realtime information on implant infection, Adjuvant therapeutic agents-Delivery Options, Nanotechnology and Nanomaterials, Nanobiotechnology- Prevention and Treatment of Orthopaedic Implant Associated Infection. In order to cut the cost, we noted the following Advances in Production and Biomaterials; Three-dimensional (3D) Printing, Customised Implants, Natural Medicinal Compounds in Bone Tissue Engineering, Cost Effectiveness of 3D Printing implants, Meta-biomaterials, Functionalization of 3D-printed titanium alloy orthopedic implants. We noted the following advances in Designs and feedback of Prosthetics; SMART (sensing, measuring, and advanced reporting technology) Implants, Technological Advances in Prosthesis Design and Rehabilitation, Biofeedback to Improve the Performance of Myoelectric Pattern Recognition, Neurophysiological Evaluation of Haptic Feedback for Myoelectric Prostheses. Conclusions We believe that the future of amputee rehabilitation is bright with the advances in implant manufacturing, biofeedback, and infection prevention.

Cross-user gesture recognition from sEMG signals using an optimal transport assisted student-teacher framework

The cross-user gesture recognition is a puzzle in the myoelectric control system, owing to great variability in muscle activities across different users. To address this problem, a novel optimal transport (OT) assisted student-teacher (ST) framework (termed OT-ST) was proposed in this paper to facilitate transfer across user domains in an unsupervised domain adaptation (UDA) manner. In this framework, the initial parameters of the ST models were trained with the labeled data from users in the source domain. In the model transfer stage for a new user in the target domain, the teacher model was utilized to generate pseudo labels for unlabeled testing samples, providing guidance to the adaptation of the student model. The OT algorithm was employed to optimize the pseudo labels generated from the teacher model, avoiding the model bias and further improving the effect of domain adaptation. The performance of the proposed OT-ST framework was evaluated via experiments of classifying seven hand gestures using high-density surface electromyogram (HD-sEMG) recordings from extensor digitorum muscles of eight intact-limbed subjects. The OT-ST framework yielded a high accuracy of 96.50 ± 2.88% for new users, and outperformed other common machine learning and UDA methods significantly (p < 0.01), demonstrating its effectiveness. The OT-ST framework does not require special repetitive training or any labeled data for calibration. In addition, it can incrementally learn from new testing samples and improve the recognition ability. This study provides a promising method for developing user-generic myoelectric pattern recognition, with wide applications in human-computer interaction, consumer electronics and prosthesis control.

A Unified User-Generic Framework for Myoelectric Pattern Recognition: Mix-up and Adversarial Training for Domain Generalization and Adaptation.

To address cross-user variability problem in the myoelectric pattern recognition, a novel method for domain generalization and adaptation using both mix-up and adversarial training strategies, termed MAT-DGA, is proposed in this paper. This method enables integration of domain generalization (DG) with unsupervised domain adaptation (UDA) into a unified framework. The DG process highlights user-generic information in the source domain for training a model expected to be suitable for a new user in a target domain, where the UDA process further improves the model performance with a few unlabeled testing data from the new user. In this framework, both mix-up and adversarial training strategies were also applied to each of both the DG and UDA processes by exploiting their complementary advantages towards enhanced integration of both processes. The performance of the proposed method was evaluated via experiments of classifying seven hand gestures using high-density myoelectric data recorded from extensor digitorum muscles of eight intact-limbed subjects. It yielded a high accuracy of 95.71±4.17% and outperformed other UDA methods significantly (p<0.05) under cross-user testing scenarios. Moreover, it reduced the number of calibration samples required in the UDA process (p<0.05) after its initial performance had already been lifted by the DG process. The proposed method provides an effective and promising way of establishing cross-user myoelectric pattern recognition control systems. Our work helps to promote development of user-generic myoelectric interfaces, with wide applications in motor control and health.

Deep Scattering Transform with Attention Mechanisms Improves EMG-based Hand Gesture Recognition.

Electromyogram (EMG) signals provide valuable insights into the muscles' activities supporting the different hand movements, but their analysis can be challenging due to their stochastic nature, noise, and non-stationary variations in the signal. We are pioneering the use of a unique combination of wavelet scattering transform (WST) and attention mechanisms adopted from recent sequence modelling developments of deep neural networks for the classification of EMG patterns. Our approach utilizes WST, which decomposes the signal into different frequency components, and then applies a non-linear operation to the wavelet coefficients to create a more robust representation of the extracted features. This is coupled with different variations of attention mechanisms, typically employed to focus on the most important parts of the input data by considering weighted combinations of all input vectors. By applying this technique to EMG signals, we hypothesized that improvement in the classification accuracy could be achieved by focusing on the correlation between the different muscles' activation states associated with the different hand movements. To validate the proposed hypothesis, the study was conducted using three commonly used EMG datasets collected from various environments based on laboratory and wearable devices. This approach shows significant improvement in myoelectric pattern recognition (PR) compared to other methods, with average accuracies of up to 98%.

Optimizing electrode positions on forearm to increase SNR and myoelectric pattern recognition performance

With the advances in electromyography-based human–computer interaction, particularly in myoelectric prosthetic hands, the position of electromyography electrodes has gained less attention from researchers. However, the performance of hand movement recognition significantly varies with the change of electrode position around the forearm muscles and along the length of the forearm muscles. To find the robust position on the forearm, this study performs a comprehensive and systematic investigation of the feasibility of hand movement recognition using electromyogram (EMG) signals concurrently recorded for sixteen electrode arrays on the forearm. The obtained results indicate that the electrode array placed on the middle of the extensor digitorum communis and extensor digiti minimi performs better than other electrode positions around the forearm. In addition, multiple electrode arrays placed near to elbow joint achieve a significantly higher signal to noise ratio and signal to movement artifact ratio. In addition to improved EMG signal quality, the recommended positions on the forearm significantly overperform in hand movement recognition and it is validated with five well-known feature extraction methods, various window sizes, and three classification algorithms. In this study, the electrode arrays near to elbow joint on the forearm achieve an F1 score of 98.28% to 98.80% with a linear discriminant analysis classifier. Therefore, this study clearly demonstrates the feasibility of recommended forearm positions for hand movement recognition. As these electrode positions are near to elbow joint, so, it is expected that these positions will be available in most amputees and utilized for improved hand movement recognition.

Myoelectric Pattern Recognition Using Gramian Angular Field and Convolutional Neural Networks for Muscle–Computer Interface

In the field of the muscle-computer interface, the most challenging task is extracting patterns from complex surface electromyography (sEMG) signals to improve the performance of myoelectric pattern recognition. To address this problem, a two-stage architecture, consisting of Gramian angular field (GAF)-based 2D representation and convolutional neural network (CNN)-based classification (GAF-CNN), is proposed. To explore discriminant channel features from sEMG signals, sEMG-GAF transformation is proposed for time sequence signal representation and feature modeling, in which the instantaneous values of multichannel sEMG signals are encoded in image form. A deep CNN model is introduced to extract high-level semantic features lying in image-form-based time sequence signals concerning instantaneous values for image classification. An insight analysis explains the rationale behind the advantages of the proposed method. Extensive experiments are conducted on benchmark publicly available sEMG datasets, i.e., NinaPro and CagpMyo, whose experimental results validate that the proposed GAF-CNN method is comparable to the state-of-the-art methods, as reported by previous work incorporating CNN models.

Multi-source domain generalization and adaptation toward cross-subject myoelectric pattern recognition

Objective. Myoelectric pattern recognition (MPR) has shown satisfactory performance under ideal laboratory conditions. Nevertheless, the individual variances lead to dramatic performance degradation in cross-user MPR applications. It is crucial to enable the myoelectric interface to adapt to multiple users’ surface electromyography (sEMG) distributions in practical. Approach. Domain adaptation (DA) is a promising approach to tackle cross-user challenges due to its ability to diminish the divergence between individual users’ EMG distributions and escalate model generalization performance. However, existing DA methods in sEMG control are based on single-source domain adaptation (SDA). SDA solely mixes multiple training users’ data as a combined source domain and attempts to align with a novel user. This simple data mixing manner ignores the sEMG distribution variations between disparate training users, leading to an insufficient variance elimination and lower performance. To this end, this paper proposes a multi-source synchronize domain adaptation framework with both DA and domain generalization (DG) capability. This multi-source framework aligns each source user and the new user in individual feature spaces, which better transfers the knowledge of existing users to the new user. Moreover, we retain the source-combined data to preserve the effectiveness of SDA. The property was further confirmed by evaluating the performance of the proposed method on data from nine subjects performing six tasks. Main results. Experiment results prove that the proposed multi-source framework achieved both positive DG and DA performance in a cross-user classification manner. Significance. This work demonstrates the usability and feasibility of the proposed multi-source framework in cross-user myoelectric control.

Myoelectric prosthesis hand grasp control following targeted muscle reinnervation in individuals with transradial amputation.

Despite the growing availability of multifunctional prosthetic hands, users' control and overall functional abilities with these hands remain limited. The combination of pattern recognition control and targeted muscle reinnervation (TMR) surgery, an innovative technique where amputated nerves are transferred to reinnervate new muscle targets in the residual limb, has been used to improve prosthesis control of individuals with more proximal upper limb amputations (i.e., shoulder disarticulation and transhumeral amputation). The goal of this study was to determine if prosthesis hand grasp control improves following transradial TMR surgery. Eight participants were trained to use a multi-articulating hand prosthesis under myoelectric pattern recognition control. All participated in home usage trials pre- and post-TMR surgery. Upper limb outcome measures were collected following each home trial. Three outcome measures (Southampton Hand Assessment Procedure, Jebsen-Taylor Hand Function Test, and Box and Blocks Test) improved 9-12 months post-TMR surgery compared with pre-surgery measures. The Assessment of Capacity for Myoelectric Control and Activities Measure for Upper Limb Amputees outcome measures had no difference pre- and post-surgery. An offline electromyography analysis showed a decrease in grip classification error post-TMR surgery compared to pre-TMR surgery. Additionally, a majority of subjects noted qualitative improvements in their residual limb and phantom limb sensations post-TMR. The potential for TMR surgery to result in more repeatable muscle contractions, possibly due to the reduction in pain levels and/or changes to phantom limb sensations, may increase functional use of many of the clinically available dexterous prosthetic hands.

Similarity function for one-shot learning to enhance the flexibility of myoelectric interfaces.

This study aims to develop a flexible myoelectric pattern recognition (MPR) method based on one-shot learning, which enables convenient switching across different usage scenarios, thereby reducing the re-training burden. First, a one-shot learning model based on a Siamese neural network was constructed to assess the similarity for any given sample pair. In a new scenario involving a new set of gestural categories and/or a new user, just one sample of each category was required to constitute a support set. This enabled the quick deployment of the classifier suitable for the new scenario, which decided for any unknown query sample by selecting the category whose sample in the support set was quantified to be the most like the query sample. The effectiveness of the proposed method was evaluated by experiments conducting MPR across diverse scenarios. The proposed method achieved high recognition accuracy of over 89% under the cross-scenario conditions, and it significantly outperformed other common one-shot learning methods and conventional MPR methods (p < 0.01). This study demonstrates the feasibility of applying one-shot learning to rapidly deploy myoelectric pattern classifiers in response to scenario change. It provides a valuable way of improving the flexibility of myoelectric interfaces toward intelligent gestural control with extensive applications in medical, industrial, and consumer electronics.

A Transformer-based Multi-task Learning Framework for Myoelectric Pattern Recognition Supporting Muscle Force Estimation.

Simultaneous implementation of myoelectric pattern recognition and muscle force estimation is highly demanded in building natural gestural interfaces but a challenging task due to the gesture classification accuracy degradation under varying muscle strengths. To address this problem, a novel method using transformer-based multi-task learning (MTL-Transformer) for the prediction of both myoelectric patterns and corresponding muscle strengths was proposed to describe the inherent characteristics of an individual gesture pattern under different force conditions, thereby improving the accuracy of myoelectric pattern recognition. In addition, the transformer model enabled the characterization of long-term temporal correlations to ensure precise and smooth estimation of the muscle force. The performance of the proposed MTL-Transformer framework was evaluated via experiments of classifying eleven hand gestures and estimating the corresponding muscle force simultaneously, using high-density surface electromyogram (HD-sEMG) recordings from forearm flexor muscles of eleven intact-limbed subjects. The MTL-Transformer framework yielded high classification accuracy (98.70±1.21%) and low root mean square deviation (12.59±2.76%), and outperformed other two common temporally modelling methods significantly (p < 0.05) in terms of both improved gesture recognition accuracies and reduced muscle force estimation errors. The MTL-Transformer framework is demonstrated as an effective solution for simultaneous implementation of myoelectric pattern recognition and muscle force estimation. This study promotes the development of robust and smooth myoelectric control systems, with wide applications in gestural interfaces, prosthetic and orthotic control.

Reduce the User Burden of Multiuser Myoelectric Interface via Few-Shot Domain Adaptation.

Due to physiological and anatomical variations across users, myoelectric interfaces trained by multiple users cannot be adapted to the unique hand movement patterns of the new user. Most current work requires the new user to provide one or more trials per gesture (dozens to hundreds of samples), applying domain adaptation methods to calibrate the model and achieve promising movement recognition performance. However, the user burden associated with time-consuming electromyography signal acquisition and annotation is a key factor hindering the practical application of myoelectric control. As shown in this work, once the number of calibration samples is reduced, the performance of previous cross-user myoelectric interfaces will degrade due to the lack of enough statistics to characterize the distributions. In this paper, a few-shot supervised domain adaptation (FSSDA) framework is proposed to address this issue. It aligns the distributions of different domains by calculating the distribution distances of point-wise surrogates. Specifically, we introduce a positive-negative pair distance loss to find a shared embedding subspace where each scarce sample from the new user will be closer to the positive samples and away from the negative samples of multiple users. Thus, FSSDA allows every target domain sample to be paired with all source domain samples and optimizes the feature distance between each target domain sample and the source domain samples within the same batch, instead of direct estimation of the data distribution of the target domain. The proposed method is validated on two high-density EMG datasets, which achieves the averaged recognition accuracies of 97.59% and 82.78% with only 5 samples per gesture. In addition, FSSDA is also effective even when only one sample per gesture is provided. The experimental results show that FSSDA greatly reduces the user burden and further facilitates the development of myoelectric pattern recognition techniques.

Neurostimulation artifact removal for implantable sensors improves signal clarity and decoding of motor volition.

As the demand for prosthetic limbs with reliable and multi-functional control increases, recent advances in myoelectric pattern recognition and implanted sensors have proven considerably advantageous. Additionally, sensory feedback from the prosthesis can be achieved via stimulation of the residual nerves, enabling closed-loop control over the prosthesis. However, this stimulation can cause interfering artifacts in the electromyographic (EMG) signals which deteriorate the reliability and function of the prosthesis. Here, we implement two real-time stimulation artifact removal algorithms, Template Subtraction (TS) and ε-Normalized Least Mean Squares (ε-NLMS), and investigate their performance in offline and real-time myoelectric pattern recognition in two transhumeral amputees implanted with nerve cuff and EMG electrodes. We show that both algorithms are capable of significantly improving signal-to-noise ratio (SNR) and offline pattern recognition accuracy of artifact-corrupted EMG signals. Furthermore, both algorithms improved real-time decoding of motor intention during active neurostimulation. Although these outcomes are dependent on the user-specific sensor locations and neurostimulation settings, they nonetheless represent progress toward bi-directional neuromusculoskeletal prostheses capable of multifunction control and simultaneous sensory feedback.

Multi-user myoelectric pattern recognition for shoulder motion intent detection

Myoelectric interfaces have a large range of applications in rehabilitation and prosthetic fields. The electromyographic signal (EMG) generates different patterns of activations according to a specific motion activity allowing the extraction of task-discriminant features, and leading to an efficient external device control [1]. The application of such methodologies in the motion intention detection (MID) is challenging, since this requires to predict the subjects’ intentions while attempting to generate a certain movement [2]. Despite good results have been obtained for the intra-subject scenario [3], the inter- subject generalization still remains an issue. The aim of this work was to investigate myoelectric interfaces for multi-user application on data relative to the shoulder MID. The dataset was composed by eight subjects performing six shoulder movements [4]. The signals were segmented into 150 ms windows with an overlap of 75 ms and four time-domain features were extracted. The feature matrix was split into a calibration and testing subsets. The spectral regression (SR) was used to reduce the dimension of the feature space and with the canonical correlation analysis (CCA) the reduced feature set was projected into a new space, where data related to different subjects are maximally correlated. The obtained projection coefficients were then used on the testing subset. The SVM classifier was trained on the projected calibration subset of a user A and the projected testing subset of a user B was used for testing. In this way, the classifier was trained on data belonging to a certain subject, and then tested on unseen data of a different person. Outcomes demonstrated that the presented approach boosted the classification performance, even if only one trial was used for the training session, with respect to the case in which neither the SR nor the CCA were applied. Indeed, the F1-score increased from 28% up to 53%. It can be also appreciated that increasing the number of repetitions for the calibration subset, the F1-score was further improved, up to 70% already with two repetitions. The accuracy presented a similar behaviour (Fig. 1). The coupling between SR and CCA proved to be reliable for the multi-user oriented shoulder MID. In MID problems, the movement recognition is performed relying on features extracted from a very short time period and on a transient epoch of the EMG signal, before the movement was fully achieved. For this challenging task, the present architecture proved to be reliable in an inter-subject scenario. Indeed, it provided high classification performances training on one subject and then testing on an unseen one, hence supporting the use of CCA for the development of subject-independent myoelectric interfaces.

Detecting the universal adversarial perturbations on high-density sEMG signals

Myoelectric pattern recognition is a promising approach for upper limb neuroprosthetic control. Convolutional neural networks (CNN) are increasingly used in dealing with the electromyography (EMG) signal collected by high-density electrodes due to its capacity to take full advantage of spatial information about muscle activity. However, it has been found that CNN models are very vulnerable to well-designed and tiny perturbations, such like universal adversarial perturbation (UAP). As shown in this work, the CNN-based myoelectric pattern recognition method can achieve a classification accuracy of more than 90%, but can only achieve a classification accuracy of less than 20% after the attack. This type of attack poses a big security concern to prosthetic control. To the best of our knowledge, there is no study on the detection of adversarial attacks to the myoelectric control system. In this paper, a correlation feature based on Chebyshev distance between the adjacent channels is proposed to detect the attack for EMG signals, which will serve as early warning and defense against the adversarial attacks. The performance of the detection framework is assessed with two high-density EMG datasets. The results show that our method has a detection rate of 91.39% and 93.87% for the attacks on both datasets with a latency of no more than 2 ms, which will facilitate the security of muscle–computer interfaces.

Incremental Learning and Fault-Tolerant Classifier for Myoelectric Pattern Recognition Against Multiple Bursting Interferences

Bursting interference that causes a sudden and significant change in surface electromyography (sEMG) characteristics, can reduce the stability and security of myoelectric assistive robots. Current adaptation strategies for progressive-generated interference are incapable of dealing with bursting interference. To address this problem, an incremental learning and fault-tolerant classifier (ILFTC) was proposed by combining a Gaussian mixture model (GMM) ensemble and linear discriminant analysis (LDA), in conjunction with online update and marginalization schemes. Subsequently, an ILFTC-based myoelectric pattern recognition (MPR) strategy was developed to improve the robustness of MPR against multiple interferences, including outlier motion and missing/fault data owing to electrode loosening. Experiments on hand/wrist motions were conducted to validate the anti-interference performance of the ILFTC. Experimental results showed that the ILFTC could effectively resist the two types of bursting interference and produce a significant improvement in the recognition performance over traditional classifiers, as well as the methods presented in previous studies. The results show that the proposed method has the potential to enhance the robustness of myoelectric assistive robots.