Consistent control information driven musculoskeletal model for multiday myoelectric control

Objective.Musculoskeletal model (MM)-based myoelectric interface has aroused great interest in human-machine interaction. However, the performance of electromyography (EMG)-driven MM in long-term use would be degraded owing to the inherent non-stationary characteristics of EMG signals. Here, to improve the estimation performance without retraining, we proposed a consistent muscle excitation extraction approach based on an improved non-negative matrix factorization (NMF) algorithm for MM when applied to simultaneous hand and wrist movement prediction.Approach.We added constraints andL2-norm regularization terms to the objective function of classic NMF regarding muscle weighting matrix and time-varying profiles, through which stable muscle synergies across days were identified. The resultant profiles of these synergies were then used to drive the MM. Both offline and online experiments were conducted to evaluate the performance of the proposed method in inter-day scenarios.Main results.The results demonstrated significantly better and more robust performance over several competitive methods in inter-day experiments, including machine learning methods, EMG envelope-driven MM, and classic NMF-based MM. Furthermore, the analysis of control information on different days revealed the effectiveness of the proposed method in obtaining consistent muscle excitations.Significance.The outcomes potentially provide a novel and promising pathway for the robust and zero-retraining control of myoelectric interfaces.

Soft Robotics Enables Neuroprosthetic Hand Design

Development and implementation of neuroprosthetic hands is a multidisciplinary field at the interface between humans and artificial robotic systems, which aims at replacing the sensorimotor function of the upper-limb amputees as their own. Although prosthetic hand devices with myoelectric control can be dated back to more than 70 years ago, their applications with anthropomorphic robotic mechanisms and sensory feedback functions are still at a relatively preliminary and laboratory stage. Nevertheless, a recent series of proof-of-concept studies suggest that soft robotics technology may be promising and useful in alleviating the design complexity of the dexterous mechanism and integration difficulty of multifunctional artificial skins, in particular, in the context of personalized applications. Here, we review the evolution of neuroprosthetic hands with the emerging and cutting-edge soft robotics, covering the soft and anthropomorphic prosthetic hand design and relating bidirectional neural interactions with myoelectric control and sensory feedback. We further discuss future opportunities on revolutionized mechanisms, high-performance soft sensors, and compliant neural-interaction interfaces for the next generation of neuroprosthetic hands.

Accurate and Robust Locomotion Mode Recognition Using High-Density EMG Recordings from a Single Muscle Group

Existing methods for human locomotion mode recognition often rely on using multiple bipolar electrode sensors on multiple muscle groups to accurately identify underlying motor activities. To avoid this complex setup and facilitate the translation of this technology, we introduce a single grid of high-density surface electromyography (HDsEMG) electrodes mounted on a single location (above the rectus femoris) to classify six locomotion modes in human walking. By employing a neural network, the trained model achieved average recognition accuracy of 97.7% with 160ms latency, significantly better than the model trained with one bipolar electrode pair placed on the same muscle (71.4% accuracy). To further exploit the spatial and temporal information of HDsEMG, we applied data augmentation to generate artificial data from simulated displaced electrodes, aiming to counteract the influence of electrode shifts. By employing a convolutional neural network with the enhanced dataset, the updated model was not strongly affected by electrode misplacement (93.9% accuracy) while models trained by bipolar electrode data were significantly disrupted by electrode shifts (29.4% accuracy). Findings suggest HDsEMG could be a valuable resource for mapping gait with fewer sensor locations and greater robustness. Results offer future promise for real-time control of assistive technology such as exoskeletons.

Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features

The multichannel electrode array used for electromyogram (EMG) pattern recognition provides good performance, but it has a high cost, is computationally expensive, and is inconvenient to wear. Therefore, researchers try to use as few channels as possible while maintaining improved pattern recognition performance. However, minimizing the number of channels affects the performance due to the least separable margin among the movements possessing weak signal strengths. To meet these challenges, two time-domain features based on nonlinear scaling, the log of the mean absolute value (LMAV) and the nonlinear scaled value (NSV), are proposed. In this study, we validate the proposed features on two datasets, the existing four feature extraction methods, variable window size, and various signal-to-noise ratios (SNR). In addition, we also propose a feature extraction method where the LMAV and NSV are grouped with the existing 11 time-domain features. The proposed feature extraction method enhances accuracy, sensitivity, specificity, precision, and F1 score by 1.00%, 5.01%, 0.55%, 4.71%, and 5.06% for dataset 1, and 1.18%, 5.90%, 0.66%, 5.63%, and 6.04% for dataset 2, respectively. Therefore, the experimental results strongly suggest the proposed feature extraction method, for taking a step forward with regard to improved myoelectric pattern recognition performance.

A musculoskeletal model driven by muscle synergy-derived excitations for hand and wrist movements

OBJECTIVE

Musculoskeletal model (MM) driven by electromyography (EMG) signals has been identified as a promising approach to predicting human motions in the control of prostheses and robots. However, muscle excitations in MMs are generally derived from the EMG signals of the targeted sensor covering the muscle, inconsistent with the fact that signals of a sensor are from multiple muscles considering signal crosstalk in actual situation. To identify more accurate muscle excitations for MM in the presence of crosstalk, we proposed a novel excitation-extracting method inspired by muscle synergy for simultaneously estimating hand and wrist movements.

APPROACH

Muscle excitations were firstly extracted using a two-step muscle synergy-derived method. Specifically, we calculated subject-specific muscle weighting matrix and corresponding profiles according to contributions of different muscles for movements derived from synergistic motion relation. Then, the improved excitations were used to simultaneously estimate hand and wrist movements through musculoskeletal modeling. Moreover, the offline comparison among the proposed method, traditional MM and regression methods, and an online test of the proposed method were conducted.

MAIN RESULTS

The offline experiments demonstrated that the proposed approach outperformed the EMG envelope-driven MM and three regression models with higher R and lower NRMSE. Furthermore, the comparison of excitations of two MMs validated the effectiveness of the proposed approach in extracting muscle excitations in the presence of crosstalk. The online test further indicated the superior performance of the proposed method than the MM driven by EMG envelopes.

SIGNIFICANCE

The proposed excitation-extracting method identified more accurate neural commands for MMs, providing a promising approach in rehabilitation and robot control to model the transformation from surface EMG to joint kinematics.

Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey

Hands are vital in a wide range of fundamental daily activities, and neurological diseases that impede hand function can significantly affect quality of life. Wearable hand gesture interfaces hold promise to restore and assist hand function and to enhance human-human and human-computer communication. The purpose of this review is to synthesize current novel sensing interfaces and algorithms for hand gesture recognition, and the scope of applications covers rehabilitation, prosthesis control, sign language recognition, and human-computer interaction. Results showed that electrical, dynamic, acoustical/vibratory, and optical sensing were the primary input modalities in gesture recognition interfaces. Two categories of algorithms were identified: 1) classification algorithms for predefined, fixed hand poses and 2) regression algorithms for continuous finger and wrist joint angles. Conventional machine learning algorithms, including linear discriminant analysis, support vector machines, random forests, and non-negative matrix factorization, have been widely used for a variety of gesture recognition applications, and deep learning algorithms have more recently been applied to further facilitate the complex relationship between sensor signals and multi-articulated hand postures. Future research should focus on increasing recognition accuracy with larger hand gesture datasets, improving reliability and robustness for daily use outside of the laboratory, and developing softer, less obtrusive interfaces.

Improved High-Density Myoelectric Pattern Recognition Control Against Electrode Shift Using Data Augmentation and Dilated Convolutional Neural Network

OBJECTIVE

the objective of this work is to develop a robust method for myoelectric control towards alleviating the interference of electrode shift. M ethods: In the proposed method, a preprocessing approach was first performed to convert high-density surface electromyogram (HD-sEMG) signals into a series of images, and the electrode shift appeared as pixel shift in these images. Next, a data augmentation approach was applied to the training data from just one position (no shift), so as to simulate HD-sEMG images derived from fictitious shift positions. The dilated convolutional neural network (DCNN) was subsequently adopted for classification. Compared to common convolutional neural network, DCNN always contained a larger receptive field that was supposed to be adept at mining wider spatial contextual information in images. This property was further confirmed to facilitate the classification of myoelectric patterns using HD-sEMG. The performance of the proposed method was evaluated with HD-sEMG data recorded by a 10×10 electrode array placed over forearm extensors of ten subjects during their performance of six wrist and finger extension tasks.

RESULTS

Under a variety of actual electrode shift conditions, the proposed method achieved a mean classification accuracy of 95.34%, and it outperformed other common methods.

CONCLUSION

This work demonstrated feasibility and usability of combining data augmentation and DCNN in predicting myoelectric patterns in the context of electrode shifts.

SIGNIFICANCE

The proposed method is a practical solution for robust myoelectric control against electrode array shifts.

Position Identification for Robust Myoelectric Control Against Electrode Shift

The vulnerability to the electrode shift was one of the key barriers to the wide application of pattern recognition-based (PR-based) myoelectric control systems outside the controlled laboratory conditions. To overcome this challenge, a novel framework named position identification (PI) was proposed. In the PI framework, an anchor gesture performed by the user was first analyzed to identify the current electrode position from a pool of potential electrode shift positions. Next, the classifier calibrated by the data of the identified position would be selected for following myoelectric control tasks. The results of the amputee and able-bodied participants both demonstrated that the differential filter combined with majority voting improved the PI accuracy. With only one second contraction of the chosen anchor gesture (hand close), the subsequent PR-based myoelectric control performance was fully restored from eight different electrode shift scenarios, with 1 cm in either or both perpendicular and parallel directions. The classification accuracies with PI framework were not significant before and after the shift ( 0.001). The advantage of restoring performance fully in just one second made it a practical solution to improve the robustness of PR-based myoelectric control systems in a wide range of real-world applications.

Efficient correction of armband rotation for myoelectric-based gesture control interface

OBJECTIVE

The appearance of commercial myoelectric armbands has greatly increased the portability and convenience of myoelectric controlled interfaces (MCIs). However, one limitation of the current state-of-the-art myoelectric control algorithms is that they have poor robustness against armband displacements, especially rotation, leading to great algorithmic performance degradation. The traditional remedy, retraining the interface, requires the data collection of all gestures and is impractical in many applications. The recently proposed position verification (PV) framework focused on quickly identifying and correcting the electrode positions after the displacement, showing the potential to restore the performance of MCI in a faster way. However, its online effectiveness is still yet to be validated.

APPROACH

This work proposed a novel algorithm of identifying the rotation direction to improve the efficiency of the PV framework and demonstrated the real-time capability of the PV framework using a commercially available armband.

MAIN RESULTS

The results showed that with PV, a 1.5-cm rotation could be corrected with an average of 3.1 ± 1.5 interactive adjustments, equivalent to around 15.5 ± 7.5 s, which was greatly reduced compared to retraining. There was no significant difference in the real-time control performance between before the armband displacement and after the PV correction.

SIGNIFICANCE

To the best of our knowledge, this study was the first maintaining pattern recognition-based myoelectric control performance in the presence of electrode shifts without recollecting the entire training data. It suggested the feasibility of the PV framework used in the myoelectric armband and MCI for practical applications.