Subspace based adaptive denoising of surface EMG from neurological injury patients

Long short-term memory (LSTM) recurrent neural network for muscle activity detection

BACKGROUND

The accurate temporal analysis of muscle activation is of great interest in many research areas, spanning from neurorobotic systems to the assessment of altered locomotion patterns in orthopedic and neurological patients and the monitoring of their motor rehabilitation. The performance of the existing muscle activity detectors is strongly affected by both the SNR of the surface electromyography (sEMG) signals and the set of features used to detect the activation intervals. This work aims at introducing and validating a powerful approach to detect muscle activation intervals from sEMG signals, based on long short-term memory (LSTM) recurrent neural networks.

METHODS

First, the applicability of the proposed LSTM-based muscle activity detector (LSTM-MAD) is studied through simulated sEMG signals, comparing the LSTM-MAD performance against other two widely used approaches, i.e., the standard approach based on Teager-Kaiser Energy Operator (TKEO) and the traditional approach, used in clinical gait analysis, based on a double-threshold statistical detector (Stat). Second, the effect of the Signal-to-Noise Ratio (SNR) on the performance of the LSTM-MAD is assessed considering simulated signals with nine different SNR values. Finally, the newly introduced approach is validated on real sEMG signals, acquired during both physiological and pathological gait. Electromyography recordings from a total of 20 subjects (8 healthy individuals, 6 orthopedic patients, and 6 neurological patients) were included in the analysis.

RESULTS

The proposed algorithm overcomes the main limitations of the other tested approaches and it works directly on sEMG signals, without the need for background-noise and SNR estimation (as in Stat). Results demonstrate that LSTM-MAD outperforms the other approaches, revealing higher values of F1-score (F1-score > 0.91) and Jaccard similarity index (Jaccard > 0.85), and lower values of onset/offset bias (average absolute bias < 6 ms), both on simulated and real sEMG signals. Moreover, the advantages of using the LSTM-MAD algorithm are particularly evident for signals featuring a low to medium SNR.

CONCLUSIONS

The presented approach LSTM-MAD revealed excellent performances against TKEO and Stat. The validation carried out both on simulated and real signals, considering normal as well as pathological motor function during locomotion, demonstrated that it can be considered a powerful tool in the accurate and effective recognition/distinction of muscle activity from background noise in sEMG signals.

Properties of the surface electromyogram following traumatic spinal cord injury: a scoping review

Traumatic spinal cord injury (SCI) disrupts spinal and supraspinal pathways, and this process is reflected in changes in surface electromyography (sEMG). sEMG is an informative complement to current clinical testing and can capture the residual motor command in great detail-including in muscles below the level of injury with seemingly absent motor activities. In this comprehensive review, we sought to describe how the sEMG properties are changed after SCI. We conducted a systematic literature search followed by a narrative review focusing on sEMG analysis techniques and signal properties post-SCI. We found that early reports were mostly focused on the qualitative analysis of sEMG patterns and evolved to semi-quantitative scores and a more detailed amplitude-based quantification. Nonetheless, recent studies are still constrained to an amplitude-based analysis of the sEMG, and there are opportunities to more broadly characterize the time- and frequency-domain properties of the signal as well as to take fuller advantage of high-density EMG techniques. We recommend the incorporation of a broader range of signal properties into the neurophysiological assessment post-SCI and the development of a greater understanding of the relation between these sEMG properties and underlying physiology. Enhanced sEMG analysis could contribute to a more complete description of the effects of SCI on upper and lower motor neuron function and their interactions, and also assist in understanding the mechanisms of change following neuromodulation or exercise therapy.

EMG-Based Continuous and Simultaneous Estimation of Arm Kinematics in Able-Bodied Individuals and Stroke Survivors

Among the potential biological signals for human-machine interactions (brain, nerve, and muscle signals), electromyography (EMG) widely used in clinical setting can be obtained non-invasively as motor commands to control movements. The aim of this study was to develop a model for continuous and simultaneous decoding of multi-joint dynamic arm movements based on multi-channel surface EMG signals crossing the joints, leading to application of myoelectrically controlled exoskeleton robots for upper-limb rehabilitation. Twenty subjects were recruited for this study including 10 stroke subjects and 10 able-bodied subjects. The subjects performed free arm reaching movements in the horizontal plane with an exoskeleton robot. The shoulder, elbow and wrist movements and surface EMG signals from six muscles crossing the three joints were recorded. A non-linear autoregressive exogenous (NARX) model was developed to continuously decode the shoulder, elbow and wrist movements based solely on the EMG signals. The shoulder, elbow and wrist movements were decoded accurately based only on the EMG inputs in all the subjects, with the variance accounted for (VAF) > 98% for all three joints. The proposed approach is capable of simultaneously and continuously decoding multi-joint movements of the human arm by taking into account the non-linear mappings between the muscle EMGs and joint movements, which may provide less effortful control of robotic exoskeletons for rehabilitation training of individuals with neurological disorders and arm impairment.

Real-Time Control of an Exoskeleton Hand Robot with Myoelectric Pattern Recognition

Robot-assisted training provides an effective approach to neurological injury rehabilitation. To meet the challenge of hand rehabilitation after neurological injuries, this study presents an advanced myoelectric pattern recognition scheme for real-time intention-driven control of a hand exoskeleton. The developed scheme detects and recognizes user’s intention of six different hand motions using four channels of surface electromyography (EMG) signals acquired from the forearm and hand muscles, and then drives the exoskeleton to assist the user accomplish the intended motion. The system was tested with eight neurologically intact subjects and two individuals with spinal cord injury (SCI). The overall control accuracy was [Formula: see text] for the neurologically intact subjects and [Formula: see text] for the SCI subjects. The total lag of the system was approximately 250[Formula: see text]ms including data acquisition, transmission and processing. One SCI subject also participated in training sessions in his second and third visits. Both the control accuracy and efficiency tended to improve. These results show great potential for applying the advanced myoelectric pattern recognition control of the wearable robotic hand system toward improving hand function after neurological injuries.