Myoelectric Pattern Recognition Research Articles

Reducing phantom limb pain using phantom motor execution enabled by augmented and virtual reality: participants’ perspectives on an innovative treatment

Purpose Phantom limb pain (PLP) is a condition that greatly diminishes quality of life. Phantom motor execution (PME), enabled by myoelectric pattern recognition combined with virtual and augmented reality, is a novel treatment for PLP. Here we, aimed to describe patients’ experiences of this treatment. Material and Methods Qualitative, using semi-structured interviews one month after completed PME treatment. Twenty-one Swedish and Dutch patients (mean age 57, 16 males, 16 lower limb amputations) participated. The analysis followed the framework approach. Results The main themes were 1) treatment effects on the perception of the phantom limb, 2) living with PLP before and after treatment, and 3) facilitators and barriers to treatment. Most participants learned to control their phantom limb, perceived it more positively and as more complete. This control over the phantom limb became a tool for managing PLP. Most participants’ outlook on life and energy levels improved after treatment. Being mentally focused during treatment was important. Therapists were pivotal to the success of the treatment. Conclusion Controlling the phantom limb improved their perception of it and pain management, self-agency, and quality of life. The therapists’ role was invaluable. We suggest training for patients in phantom limb control before and after amputation.

Analysis of electrode locations on limb condition effect for myoelectric pattern recognition

BackgroundGesture recognition using surface electromyography (sEMG) has garnered significant attention due to its potential for intuitive and natural control in wearable human–machine interfaces. However, ensuring robustness remains essential and is currently the primary challenge for practical applications.MethodsThis study investigates the impact of limb conditions and analyzes the influence of electrode placement. Both static and dynamic limb conditions were examined using electrodes positioned on the wrist, elbow, and the midpoint between them. Initially, we compared classification performance across various training conditions at these three electrode locations. Subsequently, a feature space analysis was conducted to quantify the effects of limb conditions. Finally, strategies for group training and feature selection were explored to mitigate these effects.ResultsThe results indicate that with the state-of-the-art method, classification performance at the wrist was comparable to that at the middle position, both of which outperformed the elbow, consistent with the findings from the feature space analysis. In inter-condition classification, training under dynamic limb conditions yielded better results than training under static conditions, especially at the positions covered by dynamic training. Additionally, fast and slow movement speeds produced similar performance outcomes. To mitigate the effects of limb conditions, adding more training conditions reduced classification errors; however, this reduction plateaued after four conditions, resulting in classification errors of 22.72%, 22.65%, and 26.58% for the wrist, middle, and elbow, respectively. Feature selection further improved classification performance, reducing errors to 19.98%, 19.75%, and 27.14% at the respective electrode locations, using three optimal features derived from single-condition training.ConclusionsThe study demonstrated that the impact of limb conditions was mitigated when electrodes were placed near the wrist. Dynamic limb condition training, combined with feature optimization, proved to be an effective strategy for reducing this effect. This work contributes to enhancing the robustness of myoelectric-controlled interfaces, thereby advancing the development of wearable intelligent devices.

A robust myoelectric pattern recognition framework based on individual motor unit activities against electrode array shifts

Background and objectiveElectrode shift is always one of the critical factors to compromise the performance of myoelectric pattern recognition (MPR) based on surface electromyogram (SEMG). However, current studies focused on the global features of SEMG signals to mitigate this issue but it is just an oversimplified description of the human movements without incorporating microscopic neural drive information. The objective of this work is to develop a novel method for calibrating the electrode array shifts toward achieving robust MPR, leveraging individual motor unit (MU) activities obtained through advanced SEMG decomposition. MethodsAll of the MUs from decomposition of SEMG data recorded at the original electrode array position were first initialized to train a neural network for pattern recognition. A part of decomposed MUs could be tracked and paired with MUs obtained at the original position based on spatial distribution of their MUAP waveforms, so as to determine the shift vector (describing both the orientation and distance of the shift) implicated consistently by these multiple MU pairs. Given the known shift vector, the features of the after-shift decomposed MUs were corrected accordingly and then fed into the network to finalize the MPR task. The performance of the proposed method was evaluated with data recorded by a 16 × 8 electrode array placed over the finger extensor muscles of 8 subjects performing 10 finger movement patterns. ResultsThe proposed method achieved a shift detection accuracy of 100 % and a pattern recognition accuracy approximating to 100 %, significantly outperforming the conventional methods with lower shift detection accuracies and lower pattern recognition accuracies (p < 0.05). ConclusionsOur method demonstrated the feasibility of using decomposed MUAP waveforms’ spatial distributions to calibrate electrode shift. This study provides a new tool to enhance the robustness of myoelectric control systems via microscopic neural drive information at an individual MU level.

Feature evaluation for myoelectric pattern recognition of multiple nearby reaching targets

Intention detection of the reaching movement is considerable for myoelectric human and machine collaboration applications. A comprehensive set of handcrafted features was mined from windows of electromyogram (EMG) of the upper-limb muscles while reaching nine nearby targets like activities of daily living. The feature selection-based scoring method, neighborhood component analysis (NCA), selected the relevant feature subset. Finally, the target was recognized by the support vector machine (SVM) model. The classification performance was generalized by a nested cross-validation structure that selected the optimal feature subset in the inner loop. According to the low spatial resolution of the target location on display and following the slight discrimination of signals between targets, the best classification accuracy of 77.11 % was achieved for concatenating the features of two segments with a length of 2 and 0.25 s. Due to the lack of subtle variation in EMG, while reaching different targets, a wide range of features was applied to consider additional aspects of the knowledge contained in EMG signals. Furthermore, since NCA selected features that provided more discriminant power, it became achievable to employ various combinations of features and even concatenated features extracted from different movement parts to improve classification performance.

A geometric algebra-based approach for myoelectric pattern recognition control and faster prosthesis recalibration

ObjectiveAlthough many advancements have been made on myoelectric pattern-recognition, the control of poly-articulated upper-limb prostheses remains insufficiently robust. Electrode-shift, sweat or fatigue degrade the performance of classifiers over time, resulting in unfruitful device usage and frequent re-calibration. To tackle this issue, here we introduce two models − µP6 and µP8 − that combine Geometric Algebra with nearest-neighbor classification. We aim at reducing both the necessary training data and training time and, unlike most current state-of-the-art algorithms, we exploit an alternative geometric representation (and visualization) of the EMG signal as different polygons for different types of gestures, facilitating the explanation of the decision-making process to a layman. Moreover, we explore four abstention strategies to reduce the number of misclassifications. MethodsWe perform an offline analysis on two datasets, alongside two other standard models: nonlinear logistic regression (NLR) and linear discriminant analysis (LDA). ResultsEven with few training data, the proposed algorithms achieve high F1-scores (>0.95), significantly higher or non-significantly different from the values obtained with NLR and LDA, while maintaining relatively low abstentions rates and training times (<2 ms). ConclusionThe proposed algorithms allow to reduce the amount of training data and training times without compromising recognition rates. SignificanceThe proposed algorithms may contribute for a faster prosthesis re-calibration procedure while allowing to re-gain high recognition rates. Furthermore, the decision-making process is explainable and interpretable, potentially improving user trust and acceptance.

Performance in myoelectric pattern recognition improves with transcranial direct current stimulation

Sensorimotor impairments, resulting from conditions like stroke and amputations, can profoundly impact an individual’s functional abilities and overall quality of life. Assistive and rehabilitation devices such as prostheses, exo-skeletons, and serious gaming in virtual environments can help to restore some degree of function and alleviate pain after sensorimotor impairments. Myoelectric pattern recognition (MPR) has gained popularity in the past decades as it provides superior control over said devices, and therefore efforts to facilitate and improve performance in MPR can result in better rehabilitation outcomes. One possibility to enhance MPR is to employ transcranial direct current stimulation (tDCS) to facilitate motor learning. Twelve healthy able-bodied individuals participated in this crossover study to determine the effect of tDCS on MPR performance. Baseline training was followed by two sessions of either sham or anodal tDCS using the dominant and non-dominant arms. Assignments were randomized, and the MPR task consisted of 11 different hand/wrist movements, including rest or no movement. Surface electrodes were used to record EMG and the MPR open-source platform, BioPatRec, was used for decoding motor volition in real-time. The motion test was used to evaluate performance. We hypothesized that using anodal tDCS to increase the excitability of the primary motor cortex associated with non-dominant side in able-bodied individuals, will improve motor learning and thus MPR performance. Overall, we found that tDCS enhanced MPR performance, particularly in the non-dominant side. We were able to reject the null hypothesis and improvements in the motion test’s completion rate during tDCS (28% change, p-value: 0.023) indicate its potential as an adjunctive tool to enhance MPR and motor learning. tDCS appears promising as a tool to enhance the learning phase of using assistive devices using MPR, such as myoelectric prostheses.

Using Serious Games to Measure Upper-Limb Myoelectric Pattern Recognition Prosthesis Control Performance in an At-Home Environment

Using Serious Games to Measure Upper-Limb Myoelectric Pattern Recognition Prosthesis Control Performance in an At-Home Environment

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.

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.

Mitigating the Concurrent Interference of Electrode Shift and Loosening in Myoelectric Pattern Recognition Using Siamese Autoencoder Network.

The objective of this work is to develop a novel myoelectric pattern recognition (MPR) method to mitigate the concurrent interference of electrode shift and loosening, thereby improving the practicality of MPR-based gestural interfaces towards intelligent control. A Siamese auto-encoder network (SAEN) was established to learn robust feature representations against random occurrences of both electrode shift and loosening. The SAEN model was trained with a variety of shifted-view and masked-view feature maps, which were simulated through feature transformation operated on the original feature maps. Specifically, three mean square error (MSE) losses were devised to warrant the trained model's capability in adaptive recovery of any given interfered data. The SAEN was deployed as an independent feature extractor followed by a common support vector machine acting as the classifier. To evaluate the effectiveness of the proposed method, an eight-channel armband was adopted to collect surface electromyography (EMG) signals from nine subjects performing six gestures. Under the condition of concurrent interference, the proposed method achieved the highest classification accuracy in both offline and online testing compared to five common methods, with statistical significance (p <0.05). The proposed method was demonstrated to be effective in mitigating the electrode shift and loosening interferences. Our work offers a valuable solution for enhancing the robustness of myoelectric control systems.

Short-term hand gestures recognition based on electromyography signals

&lt;p&gt;Electromyography pattern recognition to predict limb movements can&lt;br /&gt;significantly enhance the control of the prosthesis. However, this technique&lt;br /&gt;has not yet been widely used in clinical practice. Improvements in the&lt;br /&gt;myoelectric pattern recognition (MPR) system can improve the functionality&lt;br /&gt;of the prosthesis. This study proposes new sets of time domain features to&lt;br /&gt;enhance the MPR control system. Three groups of features are evaluated, time&lt;br /&gt;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.&lt;/p&gt;

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.