Analysis of forearm muscles activity by means of new protocols of multichannel EMG signal recording and processing

Los movimientos voluntarios del cuerpo son controlados por el sistema nervioso central y periférico a través de la contracción de los músculos esqueléticos. La contracción se inicia al liberarse un neurotransmisor sobre la unión neuromuscular, iniciando la propagación de un biopotencial sobre la membrana de las fibras musculares que se desplaza hacia los tendones: el Potencial de Acción de la Unidad Motora (MUAP). La señal electromiográfica de superficie registra la activación continua de dichos potenciales sobre la superficie de la piel y constituye una valiosa herramienta para la investigación, diagnóstico y seguimiento clínico de trastornos musculares, así como para la identificación de la intención movimiento tanto en términos de dirección como de potencia. En el estudio de las enfermedades del sistema neuromuscular es necesario analizar el nivel de actividad, la capacidad de producción de fuerza, la activación muscular conjunta y la predisposición a la fatiga muscular, todos ellos asociados con factores fisiológicos que determinan la resultante contracción mioeléctrica. Además, el uso de matrices de electrodos facilita la investigación de las propiedades periféricas de las unidades motoras activas, las características anatómicas del músculo y los cambios espaciales en su activación, ocasionados por el tipo de tarea motora o la potencia de la misma. El objetivo principal de esta tesis es el diseño e implementación de protocolos experimentales y algoritmos de procesado para extraer información fiable de señales sEMG multicanal en 1 y 2 dimensiones del espacio. Dicha información ha sido interpretada y relacionada con dos patologías específicas de la extremidad superior: Epicondilitis Lateral y Lesión de Esfuerzo Repetitivo. También fue utilizada para identificar la dirección de movimiento y la fuerza asociada a la contracción muscular, cuyos patrones podrían ser de utilidad en aplicaciones donde la señal electromiográfica se utilice para controlar interfaces hombre-máquina como es el caso de terapia física basada en robots, entornos virtuales de rehabilitación o realimentación de la actividad muscular. En resumen, las aportaciones más relevantes de esta tesis son: * La definición de protocolos experimentales orientados al registro de señales sEMG en una región óptima del músculo. * Definición de índices asociados a la co-activación de diferentes músculos * Identificación de señales artefactuadas en registros multicanal * Selección de los canales mas relevantes para el análisis Extracción de un conjunto de características que permita una alta exactitud en la identificación de tareas motoras Los protocolos experimentales y los índices propuestos permitieron establecer que diversos desequilibrios entre músculos extrínsecos del antebrazo podrían desempeñar un papel clave en la fisiopatología de la epicondilitis lateral. Los resultados fueron consistentes en diferentes ejercicios y pueden definir un marco de evaluación para el seguimiento y evaluación de pacientes en programas de rehabilitación motora. Por otra parte, se encontró que las características asociadas con la distribución espacial de los MUAPs mejoran la exactitud en la identificación de la intención de movimiento. Lo que es más, las características extraídas de registros sEMG de alta densidad son más robustas que las extraídas de señales bipolares simples, no sólo por la redundancia de contacto implicada en HD-EMG, sino también porque permite monitorizar las regiones del músculo donde la amplitud de la señal es máxima y que varían con el tipo de ejercicio, permitiendo así una mejor estimación de la activación muscular mediante el análisis de los canales mas relevantes.Voluntary movements are achieved by the contraction of skeletal muscles controlled by the Central and Peripheral Nervous system. The contraction is initiated by the release of a neurotransmitter that promotes a reaction in the walls of the muscular fiber, producing a biopotential known as Motor Unit Action Potential (MUAP) that travels from the neuromuscular junction to the tendons. The surface electromyographic signal records the continuous activation of such potentials over the surface of the skin and constitutes a valuable tool for the diagnosis, monitoring and clinical research of muscular disorders as well as to infer motion intention not only regarding the direction of the movement but also its power. In the study of diseases of the neuromuscular system it is necessary to analyze the level of activity, the capacity of production of strength, the load-sharing between muscles and the probably predisposition to muscular fatigue, all of them associated with physiological factors determining the resultant muscular contraction. Moreover, the use of electrode arrays facilitate the investigation of the peripheral properties of the active Motor Units, the anatomical characteristics of the muscle and the spatial changes induced in their activation of as product of type of movement or power of the contraction.The main objective of this thesis was the design and implementation of experimental protocols, and algorithms to extract information from multichannel sEMG signals in 1 and 2 dimensions of the space. Such information was interpreted and related to pathological events associated to two upper-limb conditions: Lateral Epicondylitis and Repetitive Strain Injury. It was also used to identify the direction of movement and contraction strength which could be useful in applications concerning the use of biofeedback from EMG like in robotic- aided therapies and computer-based rehabilitation training.In summary, the most relevant contributions are:§The definition of experimental protocols intended to find optimal regions for the recording of sEMG signals. §The definition of indices associated to the co- activation of different muscles. §The detection of low-quality signals in multichannel sEMG recordings.§ The selection of the most relevant EMG channels for the analysis§The extraction of a set of features that led to high classification accuracy in the identification of tasks.The experimental protocols and the proposed indices allowed establishing that imbalances between extrinsic muscles of the forearm could play a key role in the pathophysiology of lateral epicondylalgia. Results were consistent in different types of motor task and may define an assessment framework for the monitoring and evaluation of patients during rehabilitation programs.On the other hand, it was found that features associated with the spatial distribution of the MUAPs improve the accuracy of the identification of motion intention. What is more, features extracted from high density EMG recordings are more robust not only because it implies contact redundancy but also because it allows the tracking of (task changing) skin surface areas where EMG amplitude is maximal and a better estimation of muscle activity by the proper selection of the most significant channels

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

Applications of EMG in Clinical and Sports Medicine

This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields

Central and peripheral manipulations of perceived exertion and endurance performance

Perception of effort, defined as “the conscious sensation of hard, heavy and strenuous exercise”, is known to regulate endurance performance and human behaviour. Perception of effort has recently been shown to be exacerbated by mental exertion and is also known to be a main feature of fatigue. However, to date, not only its neurophysiology but also how manipulations of perceived exertion might impact endurance performance remain poorly understood. The main aim of this thesis was to investigate how manipulations of perceived exertion might impact endurance performance. This thesis is divided in two parts: central and peripheral manipulations of perceived exertion. In each part, three experimental chapters aimed to get a better insight in the neurophysiology of perceived exertion and its impact on endurance performance. In the first part (central manipulations), we firstly investigated the impact of exacerbating perceived exertion via mental exertion involving the response inhibition process on self-paced running endurance performance. This study demonstrated that as with time to exhaustion tests, time trial performance is impaired following mental exertion leading to mental fatigue. Secondly, we investigated whether mental exertion leading to mental fatigue could alter the rate of central fatigue development during constant load whole-body exercise. This study demonstrated that the exacerbated perception of effort in presence of mental fatigue does not reflect an altered rate of central fatigue development, but is likely to be due to i) an impaired central motor command and/or ii) an alteration of the central processing of the corollary discharge. Thirdly, we investigated whether mental exertion could impact the repeatability of maximal voluntary contraction of the knee extensors. We found that contrary to submaximal exercise, force production capacity is not altered by mental exertion. Finally, these three studies demonstrated that i) mental exertion negatively impacts submaximal exercise but not maximal exercise and that ii) mental fatigue differs from central fatigue. In the second part (peripheral manipulation), we firstly developed and tested the reliability of a new endurance exercise model non-limited by the cardiorespiratory system (one leg dynamic exercise), which will be of benefits for future researches aiming to manipulate feedback from group III-IV muscle afferents. Secondly, we described neuromuscular alterations induced by this exercise and tested a new methodology to indirectly measure feedback from group III-IV muscle afferents. This study demonstrated that one leg dynamic exercise induced central and peripheral fatigue and also a decrease in spinal excitability associated with an increase in cortical excitability. Furthermore, this study also suggests that monitoring cardiovascular responses during muscle occlusion might be a suitable tool to indirectly measure feedback from group III-IV muscle afferents. Thirdly, we tested the corollary discharge and afferent feedback model of perceived exertion with electromyostimulation. This study demonstrated for the first time that for the same force output, perception of effort generation is independent of muscle afferents and reflects the magnitude of the central motor command (manipulated by electromyostimulation). All together, these findings provide further evidence in support of the corollary discharge model of perceived exertion, and provide a new exercise model to investigate and manipulate perception of effort. This thesis, when integrating both experimental parts, provides new insight on how perception of effort regulates endurance performance. Specifically, it demonstrates how muscle fatigue is a contributor of the continuous increase in perception of effort during endurance exercise, but also that other contributors play a role in this increase in perception of effort. Indeed, we demonstrated for the first time that i) perception of effort alterations in the presence of mental fatigue is independent of any alterations of the neuromuscular system, and ii) muscle afferents does not directly impact perception of effort, but may influence it indirectly via their role in motor control

Fractal features of surface electromyogram: a new measure for low level muscle activation

Identifying finger and wrist flexion based actions using single channel surface electromyogram have a number of rehabilitation, defence and human computer interface applications. These applications are currently infeasible because of unreliability in classification of sEMG when the level of muscle contraction is low and when there are multiple active muscles. The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when muscles are weakly active such as during maintained wrist and finger flexion. It has been established in literature that surface electromyogram (sEMG) and other such biosignals are fractal signals. Some researchers have determined that fractal dimension (FD) is related to strength of muscle contraction. On careful analysis of fractal properties of sEMG, this research work has established that FD is related to the muscle size and complexity and not to the strength of muscle contraction. The work has also identified a novel feature, maximum fractal length (MFL) of the signal, as a good measure of strength of contraction of the muscle. From the analysis, it is observed that while at high level of contraction, root mean square (RMS) is an indicator of strength of contraction of the muscle, this relationship is not very strong when the muscle contraction is less than 50% maximum voluntary contraction. This work has established that MFL is a more reliable measure of strength of contraction compared to RMS, especially at low levels of contraction. This research work reports the use of fractal properties of sEMG to identify the small changes in strength of muscle contraction and the location of the active muscles. It is observed that fractal dimension (FD) of the signal is related with the properties of the muscle while maximum fractal length (MFL) is related to the strength of contraction of the associated muscle. The results show that classifying MFL and FD of a single channel sEMG from the forearm it is possible to accurately identify a set of finger and wrist flexion based actions even when the muscle activity is very weak. It is proposed that such a system could be used to control a prosthetic hand or for human computer interface

Mechanisms of Fatigability in People with Type 2 Diabetes and Prediabetes

Dynamic fatiguing exercise of limb muscles is the basis of exercise training and a cornerstone of management of type 2 diabetes mellitus (T2D) and prediabetes. Little is known however, about the fatigability of limb muscles (the acute exercise induced reduction in force or power) and the involved mechanisms in people with T2D and prediabetes. Current evidence suggests that people with T2D have reduced muscle strength and power, are more fatigable after static contractions, and have physical impairments affecting activities of daily living. However, impaired function in people with T2D compared with controls is larger for dynamic than static tasks. The purpose of this dissertation was to determine the magnitude and mechanisms of fatigability in people with T2D and prediabetes after a dynamic exercise task with the knee extensor muscles. Importantly, these studies matched people with T2D and prediabetes to controls based on age, sex, physical activity and body size. The first studies determined the magnitude of fatigability and the neural and muscular mechanisms in people with T2D and controls (Study 1) and in prediabetes (Study 2). People with T2D had approximately twice the decline in both power (fatigability) and electrically-evoked muscle contractile properties than controls after the six-minute dynamic task with the knee extensor muscles. People with prediabetes also had greater fatigability (~50%) and reductions in contractile properties than controls, but less than people with T2D. The reduction in voluntary activation (neural drive to the muscle) after fatiguing exercise was not different between people with T2D, prediabetes and controls. Thus, the greater fatigability in people with T2D was due to mechanisms within the skeletal muscle rather than neural drive. Study 3 determined whether skeletal muscle blood flow could explain the greater fatigability in people with T2D. People with T2D had greater fatigability and lower blood flow after exercise than controls, and there was an association between fatigability and the exercise-induced increase in muscle blood flow after exercise. Collectively, these data suggest that people with T2D and prediabetes have greater fatigability during dynamic exercise with knee extensor muscles due to mechanisms effecting muscle contractile properties, including impaired skeletal muscle blood flo

Sistema de fisiología computacional para la medición de la fatiga muscular aplicado en rehabilitación virtual

Las tecnologías emergentes en sensores fisiológicos han impulsado el avance de nuevas terapias de rehabilitación motora, entre ellas la rehabilitación virtual. Este tipo de terapia incluye la interacción con sistemas computacionales que llevan al usuario a ser consciente de sus procesos físicos y fisiológicos internos mediante dinámicas en ambientes recreados virtualmente. Estos sistemas de fisiología computacional articulados con ambientes virtuales para la rehabilitación, permiten generar terapias que son adaptables a cada usuario y a su vez moduladas fisiológicamente con el fin de proveer asistencia durante la interacción. Es el caso de las interfaces musculares, las cuales a través de sistemas wearables han permitido integrar ambientes virtuales y videojuegos serios en procesos de rehabilitación como el biofeedback. Estas interfaces utilizan señales de electromiografía de superficie (sEMG) las cuales han sido estudiadas y caracterizadas por numerosos investigadores, mostrando su potencial uso para la detección y cuantificación de la fatiga muscular. Este trabajo recoge el desarrollo de dos sistemas de fisiología computacional, el desarrollo de un videojuego serio y 3 diferentes estudios realizados con el fin de demostrar el uso potencial de un sistema wearable y de bajo costo de sEMG en rehabilitación virtual. El primer sistema de fisiología computacional comprende el desarrollo de un toolbox para el post-procesamiento de la señal sEMG usando el software Matlab

Robust and reliable hand gesture recognition for myoelectric control

Surface Electromyography (sEMG) is a physiological signal to record the electrical activity of muscles by electrodes applied to the skin. In the context of Muscle Computer Interaction (MCI), systems are controlled by transforming myoelectric signals into interaction commands that convey the intent of user movement, mostly for rehabilitation purposes. Taking the myoeletric hand prosthetic control as an example, using sEMG recorded from the remaining muscles of the stump can be considered as the most natural way for amputees who lose their limbs to perform activities of daily living with the aid of prostheses. Although the earliest myoelectric control research can date back to the 1950s, there still exist considerable challenges to address the significant gap between academic research and industrial applications. Most recently, pattern recognition-based control is being developed rapidly to improve the dexterity of myoelectric prosthetic devices due to the recent development of machine learning and deep learning techniques. It is clear that the performance of Hand Gesture Recognition (HGR) plays an essential role in pattern recognition-based control systems. However, in reality, the tremendous success in achieving very high sEMG-based HGR accuracy (≥ 90%) reported in scientific articles produced only limited clinical or commercial impact. As many have reported, its real-time performance tends to degrade significantly as a result of many confounding factors, such as electrode shift, sweating, fatigue, and day-to-day variation. The main interest of the present thesis is, therefore, to improve the robustness of sEMG-based HGR by taking advantage of the most recent advanced deep learning techniques to address several practical concerns. Furthermore, the challenge of this research problem has been reinforced by only considering using raw sparse multichannel sEMG signals as input. Firstly, a framework for designing an uncertainty-aware sEMG-based hand gesture classifier is proposed. Applying it allows us to quickly build a model with the ability to make its inference along with explainable quantified multidimensional uncertainties. This addresses the black-box concern of the HGR process directly. Secondly, to fill the gap of lacking consensus on the definition of model reliability in this field, a proper definition of model reliability is proposed. Based on it, reliability analysis can be performed as a new dimension of evaluation to help select the best model without relying only on classification accuracy. Our extensive experimental results have shown the efficiency of the proposed reliability analysis, which encourages researchers to use it as a supplementary tool for model evaluation. Next, an uncertainty-aware model is designed based on the proposed framework to address the low robustness of hand grasp recognition. This offers an opportunity to investigate whether reliable models can achieve robust performance. The results show that the proposed model can improve the long-term robustness of hand grasp recognition by rejecting highly uncertain predictions. Finally, a simple but effective normalisation approach is proposed to improve the robustness of inter-subject HGR, thus addressing the clinical challenge of having only a limited amount of data from any individual. The comparison results show that better performance can be obtained by it compared to a state-of-the-art (SoA) transfer learning method when only one training cycle is available. In summary, this study presents promising methods to pursue an accurate, robust, and reliable classifier, which is the overarching goal for sEMG-based HGR. The direction for future work would be the inclusion of these in real-time myoelectric control applications

Fused mechanomyography and inertial measurement for human-robot interface

Human-Machine Interfaces (HMI) are the technology through which we interact with the ever-increasing quantity of smart devices surrounding us. The fundamental goal of an HMI is to facilitate robot control through uniting a human operator as the supervisor with a machine as the task executor. Sensors, actuators, and onboard intelligence have not reached the point where robotic manipulators may function with complete autonomy and therefore some form of HMI is still necessary in unstructured environments. These may include environments where direct human action is undesirable or infeasible, and situations where a robot must assist and/or interface with people. Contemporary literature has introduced concepts such as body-worn mechanical devices, instrumented gloves, inertial or electromagnetic motion tracking sensors on the arms, head, or legs, electroencephalographic (EEG) brain activity sensors, electromyographic (EMG) muscular activity sensors and camera-based (vision) interfaces to recognize hand gestures and/or track arm motions for assessment of operator intent and generation of robotic control signals. While these developments offer a wealth of future potential their utility has been largely restricted to laboratory demonstrations in controlled environments due to issues such as lack of portability and robustness and an inability to extract operator intent for both arm and hand motion. Wearable physiological sensors hold particular promise for capture of human intent/command. EMG-based gesture recognition systems in particular have received significant attention in recent literature. As wearable pervasive devices, they offer benefits over camera or physical input systems in that they neither inhibit the user physically nor constrain the user to a location where the sensors are deployed. Despite these benefits, EMG alone has yet to demonstrate the capacity to recognize both gross movement (e.g. arm motion) and finer grasping (e.g. hand movement). As such, many researchers have proposed fusing muscle activity (EMG) and motion tracking e.g. (inertial measurement) to combine arm motion and grasp intent as HMI input for manipulator control. However, such work has arguably reached a plateau since EMG suffers from interference from environmental factors which cause signal degradation over time, demands an electrical connection with the skin, and has not demonstrated the capacity to function out of controlled environments for long periods of time. This thesis proposes a new form of gesture-based interface utilising a novel combination of inertial measurement units (IMUs) and mechanomyography sensors (MMGs). The modular system permits numerous configurations of IMU to derive body kinematics in real-time and uses this to convert arm movements into control signals. Additionally, bands containing six mechanomyography sensors were used to observe muscular contractions in the forearm which are generated using specific hand motions. This combination of continuous and discrete control signals allows a large variety of smart devices to be controlled. Several methods of pattern recognition were implemented to provide accurate decoding of the mechanomyographic information, including Linear Discriminant Analysis and Support Vector Machines. Based on these techniques, accuracies of 94.5% and 94.6% respectively were achieved for 12 gesture classification. In real-time tests, accuracies of 95.6% were achieved in 5 gesture classification. It has previously been noted that MMG sensors are susceptible to motion induced interference. The thesis also established that arm pose also changes the measured signal. This thesis introduces a new method of fusing of IMU and MMG to provide a classification that is robust to both of these sources of interference. Additionally, an improvement in orientation estimation, and a new orientation estimation algorithm are proposed. These improvements to the robustness of the system provide the first solution that is able to reliably track both motion and muscle activity for extended periods of time for HMI outside a clinical environment. Application in robot teleoperation in both real-world and virtual environments were explored. With multiple degrees of freedom, robot teleoperation provides an ideal test platform for HMI devices, since it requires a combination of continuous and discrete control signals. The field of prosthetics also represents a unique challenge for HMI applications. In an ideal situation, the sensor suite should be capable of detecting the muscular activity in the residual limb which is naturally indicative of intent to perform a specific hand pose and trigger this post in the prosthetic device. Dynamic environmental conditions within a socket such as skin impedance have delayed the translation of gesture control systems into prosthetic devices, however mechanomyography sensors are unaffected by such issues. There is huge potential for a system like this to be utilised as a controller as ubiquitous computing systems become more prevalent, and as the desire for a simple, universal interface increases. Such systems have the potential to impact significantly on the quality of life of prosthetic users and others.Open Acces