A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue

Muscle fatigue is an established area of research and various types of muscle fatigue have been investigated in order to fully understand the condition. This paper gives an overview of the various non-invasive techniques available for use in automated fatigue detection, such as mechanomyography, electromyography, near-infrared spectroscopy and ultrasound for both isometric and non-isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who wish to select the most appropriate methodology for research on muscle fatigue detection or prediction, or for the development of devices that can be used in, e.g., sports scenarios to improve performance or prevent injury. To date, research on localised muscle fatigue focuses mainly on the clinical side. There is very little research carried out on the implementation of detecting/predicting fatigue using an autonomous system, although recent research on automating the process of localised muscle fatigue detection/prediction shows promising results

Techniques of EMG signal analysis: detection, processing, classification and applications

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications

ESTIMATION OF STRETCH REFLEX CONTRIBUTIONS OF WRIST USING SYSTEM IDENTIFICATION AND QUANTIFICATION OF TREMOR IN PARKINSON'S DISEASE PATIENTS

"The brain's motor control can be studied by characterizing the activity of spinal motor nuclei to brain control, expressed as motor unit activity recordable by surface electrodes". When a specific area is under consideration, the first step in investigation of the motor control system pertinent to it is the system identification of that specific body part or area. The aim of this research is to characterize the working of the brain's motor control system by carrying out system identification of the wrist joint area and quantifying tremor observed in Parkinson's disease patients. We employ the ARMAX system identification technique to gauge the intrinsic and reflexive components of wrist stiffness, in order to facilitate analysis of problems associated with Parkinson's disease. The intrinsic stiffness dynamics comprise majority of the total stiffness in the wrist joint and the reflexive stiffness dynamics contribute to the tremor characteristic commonly found in Parkinson's disease patients. The quantification of PD tremor entails using blind source separation of convolutive mixtures to obtain sources of tremor in patients suffering from movement disorders. The experimental data when treated with blind source separation reveals sources exhibiting the tremor frequency components of 3-8 Hz. System identification of stiffness dynamics and assessment of tremor can reveal the presence of additional abnormal neurological signs and early identification or diagnosis of these symptoms would be very advantageous for clinicians and will be instrumental to pave the way for better treatment of the disease

An Improved Compound Gaussian Model for Bivariate Surface EMG Signals Related to Strength Training

Recent literature suggests that the surface electromyography (sEMG) signals have non-stationary statistical characteristics specifically due to random nature of the covariance. Thus suitability of a statistical model for sEMG signals is determined by the choice of an appropriate model for describing the covariance. The purpose of this study is to propose a Compound-Gaussian (CG) model for multivariate sEMG signals in which latent variable of covariance is modeled as a random variable that follows an exponential model. The parameters of the model are estimated using the iterative Expectation Maximization (EM) algorithm. Further, a new dataset, electromyography analysis of human activities database 2 (EMAHA-DB2) is developed. Based on the model fitting analysis on the sEMG signals from EMAHA-DB2, it is found that the proposed CG model fits more closely to the empirical pdf of sEMG signals than the existing models. The proposed model is validated by visual inspection, further validated by matching central moments and better quantitative metrics in comparison with other models. The proposed compound model provides an improved fit to the statistical behavior of sEMG signals. Further, the estimate of rate parameter of the exponential model shows clear relation to the training weights. Finally, the average signal power estimates of the channels shows distinctive dependency on the training weights, the subject's training experience and the type of activity.Comment: This article supersedes arXiv:2301.05417. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Automatic signal and image-based assessments of spinal cord injury and treatments.

Spinal cord injury (SCI) is one of the most common sources of motor disabilities in humans that often deeply impact the quality of life in individuals with severe and chronic SCI. In this dissertation, we have developed advanced engineering tools to address three distinct problems that researchers, clinicians and patients are facing in SCI research. Particularly, we have proposed a fully automated stochastic framework to quantify the effects of SCI on muscle size and adipose tissue distribution in skeletal muscles by volumetric segmentation of 3-D MRI scans in individuals with chronic SCI as well as non-disabled individuals. We also developed a novel framework for robust and automatic activation detection, feature extraction and visualization of the spinal cord epidural stimulation (scES) effects across a high number of scES parameters to build individualized-maps of muscle recruitment patterns of scES. Finally, in the last part of this dissertation, we introduced an EMG time-frequency analysis framework that implements EMG spectral analysis and machine learning tools to characterize EMG patterns resulting in independent or assisted standing enabled by scES, and identify the stimulation parameters that promote muscle activation patterns more effective for standing. The neurotechnological advancements proposed in this dissertation have greatly benefited SCI research by accelerating the efforts to quantify the effects of SCI on muscle size and functionality, expanding the knowledge regarding the neurophysiological mechanisms involved in re-enabling motor function with epidural stimulation and the selection of stimulation parameters and helping the patients with complete paralysis to achieve faster motor recovery

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research