Monitoring muscle fatigue following continuous load changes

Department of Human Factors EngineeringPrevious studies related to monitoring muscle fatigue during dynamic motion have focused on detecting the accumulation of muscle fatigue. However, it is necessary to detect both accumulation and recovery of muscle fatigue in dynamic muscle contraction while muscle load changes continuously. This study aims to investigate the development and recovery of muscle fatigue in dynamic muscle contraction conditions following continuous load changes. Twenty healthy males conducted repetitive elbow flexion and extension using 2kg and 1kg dumbbell, by turns. They performed the two tasks of different intensity (2kg intensity task, 1kg intensity task) alternately until they felt they could no longer achieve the required movement range or until they experienced unacceptable biceps muscle discomfort. Meanwhile, using EMG signal of biceps brachii muscle, fatigue detections were performed from both dynamic measurements during each dynamic muscle contraction task and isometric measurements during isometric muscle contraction right before and after each task. In each of 2kg and 1kg intensity tasks, pre, post and change value of EMG amplitude (AEMG) and center frequency were computed respectively. They were compared to check the validity of the muscle fatigue monitoring method using Wavelet transform with EMG signal from dynamic measurements. As a result, a decrease of center frequency in 2kg intensity tasks and an increase of center frequency in 1kg intensity tasks were detected. It shows that development and recovery of muscle fatigue were detected in 2kg and 1kg intensity tasks, respectively. Also, the tendency of change value of center frequency from dynamic measurements were corresponded with that from isometric measurements. It suggests that monitoring muscle fatigue in dynamic muscle contraction conditions using wavelet transform was valid to detect the development and recovery of muscle fatigue continuously. The result also shows the possibility of monitoring muscle fatigue in real-time in industry and it could propose a guideline in designing a human-robot interaction system based on monitoring user's muscle fatigue.clos

Prediction of isometric motor tasks and effort levels based on high-density EMG in patients with incomplete spinal cord injury

Objective. The development of modern assistive and rehabilitation devices requires reliable and easy-to-use methods to extract neural information for control of devices. Group-specific pattern recognition identifiers are influenced by inter-subject variability. Based on high-density EMG (HD-EMG) maps, our research group has already shown that inter-subject muscle activation patterns exist in a population of healthy subjects. The aim of this paper is to analyze muscle activation patterns associated with four tasks (flexion/extension of the elbow, and supination/pronation of the forearm) at three different effort levels in a group of patients with incomplete Spinal Cord Injury (iSCI). Approach. Muscle activation patterns were evaluated by the automatic identification of these four isometric tasks along with the identification of levels of voluntary contractions. Two types of classifiers were considered in the identification: linear discriminant analysis and support vector machine. Main results. Results show that performance of classification increases when combining features extracted from intensity and spatial information of HD-EMG maps (accuracy = 97.5%). Moreover, when compared to a population with injuries at different levels, a lower variability between activation maps was obtained within a group of patients with similar injury suggesting stronger task-specific and effort-level-specific co-activation patterns, which enable better prediction results. Significance. Despite the challenge of identifying both the four tasks and the three effort levels in patients with iSCI, promising results were obtained which support the use of HD-EMG features for providing useful information regarding motion and force intentionPeer ReviewedPostprint (author's final draft

Spatial distribution of HD-EMG improves identification of task and force in patients with incomplete spinal cord injury

Background: Recent studies show that spatial distribution of High Density surface EMG maps (HD-EMG) improves the identification of tasks and their corresponding contraction levels. However, in patients with incomplete spinal cord injury (iSCI), some nerves that control muscles are damaged, leaving some muscle parts without an innervation. Therefore, HD-EMG maps in patients with iSCI are affected by the injury and they can be different for every patient. The objective of this study is to investigate the spatial distribution of intensity in HD-EMG recordings to distinguish co-activation patterns for different tasks and effort levels in patients with iSCI. These patterns are evaluated to be used for extraction of motion intention.; Method: HD-EMG was recorded in patients during four isometric tasks of the forearm at three different effort levels. A linear discriminant classifier based on intensity and spatial features of HD-EMG maps of five upper-limb muscles was used to identify the attempted tasks. Task and force identification were evaluated for each patient individually, and the reliability of the identification was tested with respect to muscle fatigue and time interval between training and identification. Results: Three feature sets were analyzed in the identification: 1) intensity of the HD-EMG map, 2) intensity and center of gravity of HD-EMG maps and 3) intensity of a single differential EMG channel (gold standard).; Results show that the combination of intensity and spatial features in classification identifies tasks and effort levels properly (Acc = 98.8 %; S = 92.5 %; P = 93.2 %; SP = 99.4 %) and outperforms significantly the other two feature sets (p < 0.05).; Conclusion: In spite of the limited motor functionality, a specific co-activation pattern for each patient exists for both intensity, and spatial distribution of myoelectric activity. The spatial distribution is less sensitive than intensity to myoelectric changes that occur due to fatigue, and other time-dependent influences.Peer ReviewedPostprint (published version

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

Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography.

The study of hand and finger movement is an important topic with applications in prosthetics, rehabilitation, and ergonomics. Surface electromyography (sEMG) is the gold standard for the analysis of muscle activation. Previous studies investigated the optimal electrode number and positioning on the forearm to obtain information representative of muscle activation and robust to movements. However, the sEMG spatial distribution on the forearm during hand and finger movements and its changes due to different hand positions has never been quantified. The aim of this work is to quantify 1) the spatial localization of surface EMG activity of distinct forearm muscles during dynamic free movements of wrist and single fingers and 2) the effect of hand position on sEMG activity distribution. The subjects performed cyclic dynamic tasks involving the wrist and the fingers. The wrist tasks and the hand opening/closing task were performed with the hand in prone and neutral positions. A sensorized glove was used for kinematics recording. sEMG signals were acquired from the forearm muscles using a grid of 112 electrodes integrated into a stretchable textile sleeve. The areas of sEMG activity have been identified by a segmentation technique after a data dimensionality reduction step based on Non Negative Matrix Factorization applied to the EMG envelopes. The results show that 1) it is possible to identify distinct areas of sEMG activity on the forearm for different fingers; 2) hand position influences sEMG activity level and spatial distribution. This work gives new quantitative information about sEMG activity distribution on the forearm in healthy subjects and provides a basis for future works on the identification of optimal electrode configuration for sEMG based control of prostheses, exoskeletons, or orthoses. An example of use of this information for the optimization of the detection system for the estimation of joint kinematics from sEMG is reported

A novel spatial feature for the identification of motor tasks using high-density electromyography

Estimation of neuromuscular intention using electromyography (EMG) and pattern recognition is still an open problem. One of the reasons is that the pattern-recognition approach is greatly influenced by temporal changes in electromyograms caused by the variations in the conductivity of the skin and/or electrodes, or physiological changes such as muscle fatigue. This paper proposes novel features for task identification extracted from the high-density electromyographic signal (HD-EMG) by applying the mean shift channel selection algorithm evaluated using a simple and fast classifier-linear discriminant analysis. HD-EMG was recorded from eight subjects during four upper-limb isometric motor tasks (flexion/extension, supination/pronation of the forearm) at three different levels of effort. Task and effort level identification showed very high classification rates in all cases. This new feature performed remarkably well particularly in the identification at very low effort levels. This could be a step towards the natural control in everyday applications where a subject could use low levels of effort to achieve motor tasks. Furthermore, it ensures reliable identification even in the presence of myoelectric fatigue and showed robustness to temporal changes in EMG, which could make it suitable in long-term applications.Peer ReviewedPostprint (published version

Muscle activation of a sportsman and an untrained man

The research was focused to analyse the muscle activation using sEMG during repeated physical activity. The purpose of this study was to find out differences between a sportsman and an untrained man in the timing of muscle activation during increasing load. Two men took part in the research. The untrained man (MN) 20 years old, 180 cm, 74 kg, sportsman (MS) 22 years old, 177 cm, 87 kg. Participants performed repeated flexion and extension in the elbow joint. Muscle activation has been tested using sEMG on the selected muscles: biceps brachii (BB), brachioradialis (B), trapezius p. descendez (TD), deltoideus p. acromialis (DA). Both participants performed test for 5, 8 and 12 kg load. Number of repetition was 10. In flexion, the time of muscle activations were the same or very similar for BB for both participants and longer in MN for B and DA, for TD were longer in MS for all loads. Differences were caused firstly by a different finishing time. In extension DA and B in MN, partly also TD were activated for a longer time. The time for activation of BB was mostly the same. Differences were caused mainly by the later start of the activation in MS. Also tendencies in prolonging and shortening the time of activation in relation to increasing load in both participants demonstrated different characteristics. Significantly longer time of muscle activation of MN has been proved only for B and DA for both kinds of movements. Differences between participants in the total time of activation at absolutely same load were expected especially because of the better movement efficiency, adaptation to the power load and movement technique in MS.The paper is the result of research project: "Institutional developing project" (RP 90518010) of Brno University of Technology. The project was financed by Brno University of Technology

Myoelectric forearm prostheses: State of the art from a user-centered perspective

User acceptance of myoelectric forearm prostheses is currently low. Awkward control, lack of feedback, and difficult training are cited as primary reasons. Recently, researchers have focused on exploiting the new possibilities offered by advancements in prosthetic technology. Alternatively, researchers could focus on prosthesis acceptance by developing functional requirements based on activities users are likely to perform. In this article, we describe the process of determining such requirements and then the application of these requirements to evaluating the state of the art in myoelectric forearm prosthesis research. As part of a needs assessment, a workshop was organized involving clinicians (representing end users), academics, and engineers. The resulting needs included an increased number of functions, lower reaction and execution times, and intuitiveness of both control and feedback systems. Reviewing the state of the art of research in the main prosthetic subsystems (electromyographic [EMG] sensing, control, and feedback) showed that modern research prototypes only partly fulfill the requirements. We found that focus should be on validating EMG-sensing results with patients, improving simultaneous control of wrist movements and grasps, deriving optimal parameters for force and position feedback, and taking into account the psychophysical aspects of feedback, such as intensity perception and spatial acuity