An Autonomous Wearable System for Predicting and Detecting Localised Muscle Fatigue

Muscle fatigue is an established area of research and various types of muscle fatigue have been clinically investigated in order to fully understand the condition. This paper demonstrates a non-invasive technique used to automate the fatigue detection and prediction process. The system utilises the clinical aspects such as kinematics and surface electromyography (sEMG) of an athlete during isometric contractions. Various signal analysis methods are used illustrating their applicability in real-time settings. This demonstrated system can be used in sports scenarios to promote muscle growth/performance or prevent injury. To date, research on localised muscle fatigue focuses on the clinical side and lacks the implementation for detecting/predicting localised muscle fatigue using an autonomous system. Results show that automating the process of localised muscle fatigue detection/prediction is promising. The autonomous fatigue system was tested on five individuals showing 90.37% accuracy on average of correct classification and an error of 4.35% in predicting the time to when fatigue will onset

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

Novel Pseudo-Wavelet function for MMG signal extraction during dynamic fatiguing contractions

The purpose of this study was to develop an algorithm to classify muscle fatigue content in sports related scenarios. Mechanomyography (MMG) signals of the biceps muscle were recorded from thirteen subjects performing dynamic contractions until fatigue. For training and testing purposes, the signals were labeled in two classes (Non-Fatigue and Fatigue). A genetic algorithm was used to evolve a pseudo-wavelet function for optimizing the detection of muscle fatigue. Tuning of the generalized evolved pseudo-wavelet function was based on the decomposition of 70% of the conducted MMG trials. After completing 25 independent pseudo-wavelet evolution runs, the best run was selected and then tested on the remaining 30% of the data to measure the classification performance. Results show that the evolved pseudo-wavelet improved the classification rate of muscle fatigue by 4.70 percentage points to 16.61 percentage points when compared to other standard wavelet functions, giving an average correct classification of 80.63%, with statistical significance (p < 0.05). © 2014 by the authors; licensee MDPI, Basel, Switzerland

Implications of Variability of Electromyographic Measurements for Assessing Localized Muscle Fatigue

The impact of work-related musculoskeletal disorders (WMSDs) is enormous due to a combination of direct and indirect costs associated with healthcare, lost workdays and human suffering. Because of the established relationship between Localized Muscle Fatigue (LMF) development and WMSDs, and in order to reduce and/or prevent WMSDs in workplaces, different fatigue assessment methods have been developed. Surface Electromyography (SEMG) is a commonly used LMF assessment technique. The SEMG signals are typically analyzed in time and frequency domains to predict LMF based on a relative change with respect to initial, or under no-fatigue conditions. Quantifying such change, however, relies on the assumption that the SEMG measures without fatigue present, under different muscular demands, can serve as an appropriate reference within the joint range-of-motion. To our knowledge, the assumption that the electromyographic measures do not change/vary due to factors other than LMF has not been thoroughly tested. Therefore, the objective of this study was to quantify variability of various SEMG measures in non-fatigued shoulder muscles and its implication for assessing muscle fatigue. In the first Specific Aim, an experiment was performed to quantify variability of six EMG measures (RMS, MAV, ZC, MnPF, MdPF, and PFB11-22 Hz) in seven non-fatigued shoulder muscles. Twelve human participants performed 120 occupationally relevant static holding tasks. The variability in SEMG data was quantified using Mean Square Error (√MSE) obtained from ANOVA models. The SEMG measures were found to vary between 5.32% to 12.25% due to factors other than muscle fatigue. The narrowest range of variability was observed for ZC (10.20% to 11.00%), and the largest range of variability was observed for MdPF (8.72% to 12.25%). In the second Specific Aim, a relationship between SEMG variability and LMF based on perceived exertion ratings was studied. Twelve human participants performed 8 fatigue inducing exertions for 10-45 seconds. The data were analyzed to identify muscle fatigue onset based on the perceived exertion ratings and the corresponding relative changes in SEMG measures. A good agreement was observed between the definition of LMF based on perceived exertion ratings and the relative change in the SEMG measures (quantified in Aim 1) for ZC, MnPF, and MdPF. And the study concludes that for the shoulder muscles a change higher than 11.00%, 11.45%, and 12.25% in ZC, MnPF, and MdPF, respectively, can be an indication of LMF. In conclusion, the study findings suggest that a change higher than 11.00%, 11.45%, and 12.25% in ZC, MnPF, and MdPF, respectively, can be an indication of LMF. These findings could be useful in improving real-time fatigue predication models and/or methods to curtail the incidence of LMF based WMSDs in workplaces

Muscle Force Estimation and Fatigue Detection Based on sEMG Signals

Ph.DDOCTOR OF PHILOSOPH

On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden