A Myoelectric Control Interface for Upper-Limb Robotic Rehabilitation Following Spinal Cord Injury

McDonald, Craig G.; Sullivan, Jennifer L.; Dennis, Troy A.; O’Malley, Marcia K.

doi:10.1109/tnsre.2020.2979743

Cited by 34 publications

(27 citation statements)

References 28 publications

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“…An example of this is reported by Wentink et al [ 3 ] who showed that the analysis of EMG signals allows assessing gait initiation earlier (63–138 ms) than inertial sensors, in a population of transfemoral amputees. In this and other cases, EMG-based approach seems to be really valuable [ 4 – 6 ]. Moreover, in the analysis of the majority of neuromuscular diseases, such as for example spastic cerebral palsy, the acquisition of sEMG signals is essential.…”

Section: Introductionmentioning

confidence: 99%

Intra-subject approach for gait-event prediction by neural network interpretation of EMG signals

et al. 2020

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Background Gait analysis is acknowledged as the main approach for quantitatively assessing the alteration of motor function in different contexts, such as in basic research and clinics. Technological development is making available smart and wearable sensors (inertial Abstract Background: Machine learning models were satisfactorily implemented for estimating gait events from surface electromyographic (sEMG) signals during walking. Most of them are based on inter-subject approaches for data preparation. Aim of the study is to propose an intra-subject approach for binary classifying gait phases and predicting gait events based on neural network interpretation of sEMG signals and to test the hypothesis that the intra-subject approach is able to achieve better performances compared to an inter-subject one. To this aim, sEMG signals were acquired from 10 leg muscles in about 10.000 strides from 23 healthy adults, during ground walking, and a multi-layer perceptron (MLP) architecture was implemented. Results: Classification/prediction accuracy was tested vs. the ground truth, represented by the foot-floor-contact signal provided by three foot-switches, through samples not used during training phase. Average classification accuracy of 96.1 ± 1.9% and mean absolute value (MAE) of 14.4 ± 4.7 ms and 23.7 ± 11.3 ms in predicting heel-strike (HS) and toe-off (TO) timing were provided. Performances of the proposed approach were tested by a direct comparison with performances provided by the inter-subject approach in the same population. Comparison results showed 1.4% improvement of mean classification accuracy and a significant (p < 0.05) decrease of MAE in predicting HS and TO timing (23% and 33% reduction, respectively). Conclusions: The study developed an accurate methodology for classification and prediction of gait events, based on neural network interpretation of intra-subject sEMG data, able to outperform more typical inter-subject approaches. The clinically useful contribution consists in predicting gait events from only EMG signals from a single subject, contributing to remove the need of further sensors for the direct measurement of temporal data.

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Section: Introductionmentioning

confidence: 99%

Intra-subject approach for gait-event prediction by neural network interpretation of EMG signals

et al. 2020

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“…Recent studies have demonstrated that EMG signals can be measured in muscles that are weakened or even paralyzed by SCI (9–11). Thus, measuring and decoding myoelectric signals from severely impaired muscles may provide a practical, noninvasive solution for controlling assistive devices after SCI.…”

Section: Discussionmentioning

confidence: 99%

Sensing and decoding the neural drive to paralyzed muscles during attempted movements of a person with tetraplegia using a sleeve array

Ting

Vecchio²,

Sarma

et al. 2021

Preprint

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Motor neurons in the brain and spinal cord convey information about motor intent that can be extracted and interpreted to control assistive devices, such as computers, wheelchairs, and robotic manipulators. However, most methods for measuring the firing activity of single neurons rely on implanted microelectrodes. Although intracortical brain-computer interfaces (BCIs) have been shown to be safe and effective, the requirement for surgery poses a barrier to widespread use. Here, we demonstrate that a wearable sensor array can detect residual motor unit activity in paralyzed muscles after severe cervical spinal cord injury (SCI). Despite generating no observable hand movement, volitional recruitment of motor neurons below the level of injury was observed across attempted movements of individual fingers and overt wrist and elbow movements. Subgroups of motor units were coactive during flexion or extension phases of the task. Single digit movement intentions were classified offline from the EMG power (RMS) or motor unit firing rates with median classification accuracies >75% in both cases. Simulated online control of a virtual hand was performed with a binary classifier to test feasibility of real time extraction and decoding of motor units. The online decomposition algorithm extracted motor units in 1.2 ms, and the firing rates predicted the correct digit motion 88 ± 24% of the time. This study provides the first demonstration of a wearable interface for recording and decoding firing rates of motor neurons below the level of injury in a person with tetraplegia after motor complete SCI.

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“…EMG-force regression was performed on the downsampled features and force values using training data. The first 2 s and the last 2 s of the data were removed to account for filter startup and tail transients 1 . We trained linear finite impulse response models (to provide dynamics) of the form:…”

Section: B Pattern Recognition Of Hand Gestures 1) Methods For Pattern Recognition: (11) Lda-based Methodsmentioning

confidence: 99%

“…S URFACE electromyogram (sEMG)-based neural interface techniques [1] have attracted increasing attention in recent years. Neural interfaces help amputees regain function via neuroprostheses which can be intuitively controlled by sEMG signals from residual muscles in the stump.…”

Section: Introductionmentioning

confidence: 99%

Open Access Dataset, Toolbox and Benchmark Processing Results of High-Density Surface Electromyogram Recordings

Jiang

Liu

Fan

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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We provide an open access dataset of High densitY Surface Electromyogram (HD-sEMG) Recordings (named "Hyser"), a toolbox for neural interface research, and benchmark results for pattern recognition and EMG-force applications. Data from 20 subjects were acquired twice per subject on different days following the same experimental paradigm. We acquired 256-channel HD-sEMG from forearm muscles during dexterous finger manipulations. This Hyser dataset contains five sub-datasets as: (1) pattern recognition (PR) dataset acquired during 34 commonly used hand gestures, (2) maximal voluntary muscle contraction (MVC) dataset while subjects contracted each individual finger, (3) one-degree of freedom (DoF) dataset acquired during force-varying contraction of each individual finger, (4) N-DoF dataset acquired during prescribed contractions of combinations of multiple fingers, and (5) random task dataset acquired during random contraction of combinations of fingers without any prescribed force trajectory. Dataset 1 can be used for gesture recognition studies. Datasets 2-5 also recorded individual finger forces, thus can be used for studies on proportional control of neuroprostheses. Our toolbox can be used to: (1) analyze each of the five datasets using standard benchmark methods and (2) decompose HD-sEMG signals into motor unit action potentials via independent component analysis. We expect our dataset, toolbox and benchmark analyses can provide a unique platform to promote a wide range of neural interface research and collaboration among neural rehabilitation engineers.

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A Myoelectric Control Interface for Upper-Limb Robotic Rehabilitation Following Spinal Cord Injury

Cited by 34 publications

References 28 publications

Intra-subject approach for gait-event prediction by neural network interpretation of EMG signals

Intra-subject approach for gait-event prediction by neural network interpretation of EMG signals

Sensing and decoding the neural drive to paralyzed muscles during attempted movements of a person with tetraplegia using a sleeve array

Open Access Dataset, Toolbox and Benchmark Processing Results of High-Density Surface Electromyogram Recordings

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