BiLSTM-Based Joint Torque Prediction From Mechanomyogram During Isometric Contractions: A Proof of Concept Study.

Quantifying muscle strength is an important measure in clinical settings; however, there is a lack of practical tools that can be deployed for routine assessment. The purpose of this study is to propose a deep learning model for ankle plantar flexion torque prediction from time-series mechanomyogram (MMG) signals recorded during isometric contractions (i.e., a similar form to manual muscle testing procedure in clinical practice) and to evaluate its performance. Four different deep learning models in terms of model architecture (based on a stacked bidirectional long short-term memory and dense layers) were designed with different combinations of the number of units (from 32 to 512) and dropout ratio (from 0.0 to 0.8), and then evaluated for prediction performance by conducting the leave-one-subject-out cross-validation method from the 10-subject dataset. As a result, the models explained more variance in the untrained test dataset as the error metrics (e.g., root-mean-square error) decreased and as the slope of the relationship between the measured and predicted joint torques became closer to 1.0. Although the slope estimates appear to be sensitive to an individual dataset, >70% of the variance in nine out of 10 datasets was explained by the optimal model. These results demonstrated the feasibility of the proposed model as a potential tool to quantify average joint torque during a sustained isometric contraction.

Motor unit innervation zone localization based on robust linear regression analysis.

With the aim of developing a flexible and reliable procedure for superficial muscle innervation zone (IZ) localization, we proposed a method to estimate IZ location using surface electromyogram (EMG) based on robust linear regression. Regression lines were used to model the bidirectional propagation pattern of a single motor unit action potential (MUAP) and visualize the trajectory of the MUAP propagation. IZ localization was performed by identifying the origin of the bidirectional MUAP propagation. Robust linear regression and MUAP peak detection, combined with propagation phase reversal identification, may provide an efficient way to estimate IZ location. Our method offers high resolution in locating IZs based on simulation studies and experimental tests. Furthermore, our method is flexible and may also be applied using a relatively small number of EMG channels. A comparative study of the proposed method with the cross-correlation method for IZ localization was conducted. The results obtained with simulated MUAPs and measured spontaneous MUAPs in the biceps brachii muscle in six subjects (four males and two females, 57 ±â€¯10 years old) with amyotrophic lateral sclerosis (ALS). Our method achieved estimation performance comparable to that obtained by using the cross-correlation method but with higher resolution. This study provides an accurate and practical method to estimate IZ location.

Subspace based adaptive denoising of surface EMG from neurological injury patients.

OBJECTIVE: After neurological injuries such as spinal cord injury, voluntary surface electromyogram (EMG) signals recorded from affected muscles are often corrupted by interferences, such as spurious involuntary spikes and background noises produced by physiological and extrinsic/accidental origins, imposing difficulties for signal processing. Conventional methods did not well address the problem caused by interferences. It is difficult to mitigate such interferences using conventional methods. The aim of this study was to develop a subspace-based denoising method to suppress involuntary background spikes contaminating voluntary surface EMG recordings. APPROACH: The Karhunen-Loeve transform was utilized to decompose a noisy signal into a signal subspace and a noise subspace. An optimal estimate of EMG signal is derived from the signal subspace and the noise power. Specifically, this estimator is capable of making a tradeoff between interference reduction and signal distortion. Since the estimator partially relies on the estimate of noise power, an adaptive method was presented to sequentially track the variation of interference power. The proposed method was evaluated using both semi-synthetic and real surface EMG signals. MAIN RESULTS: The experiments confirmed that the proposed method can effectively suppress interferences while keep the distortion of voluntary EMG signal in a low level. The proposed method can greatly facilitate further signal processing, such as onset detection of voluntary muscle activity. SIGNIFICANCE: The proposed method can provide a powerful tool for suppressing background spikes and noise contaminating voluntary surface EMG signals of paretic muscles after neurological injuries, which is of great importance for their multi-purpose applications.

Functional restoration for the stroke survivor: informing the efforts of engineers.

As bioengineers begin to notice the importance of therapy in the recovery from stroke and other brain injuries, new technologies will be increasingly conceived, adapted, and designed to improve the patient's road to recovery. What is clear from engineering history, however, is that the best engineering efforts are often built on strong scientific foundations. In an effort to inform engineers with the necessary background on cutting edge research in the field of stroke and motor recovery, this article summarizes the views of several experts in the field as a result of a workshop held in 2006 on the topic. Here we elaborate on several areas relevant to this goal, including the pathophysiology of stroke and stroke recovery, the biomechanics, the secondary peripheral changes in muscle and other tissue, and the results of neuroimaging studies. One conclusion is that the current state of knowledge is now ripe for research using machines but that highly sophisticated robotic devices may not yet be needed. Instead, what may be needed is basic evidence that shows a difference in one therapeutic strategy over another.