Age-Related EMG Responses Of The Biceps Brachii Muscle Of Young Adults

Although the effect of an Electromyographic (EMG) signal on the Biceps Brachii (BB) muscle is at the forefront of human movement analysis,there is limited information regarding the importance of the differences in the age-related EMG responses during contraction.The present study aimed to compare the BB muscle activity of three different groups of young adults divided based on age and to find a relationship between surface EMG and endurance time during isometric contraction.The EMG signal was recorded in 30 healthy right-arm-dominant young male subjects during a handgrip force task.The subjects were rationally divided into one of the three age groups (ten in each group):adolescents (‘A’;aged 17.3 ± 1.4 years), vicenarians (‘V’; 24.6 ± 2.1 years),and tricenarians (‘T’; 33.2 ± 1.1 years).The muscle activation during contraction was determined as the root mean square (RMS) EMG signal normalised to the peak RMS EMG signal during a 10-s isometric contraction.The statistical analysis included linear regression to examine the relationship between the EMG amplitude and the endurance time based on five levels of contraction [60%,70%,80%,90% and 100% of the maximal voluntary contraction (MVC)],repeated measures ANOVA to assess differences among the different age groups and the coefficient of variation (CoV) to investigate the steadiness of the EMG activation. The result shows that the early age groups exhibit higher and steadier muscle activity (V: 3.65 ± 0.42 mV,11.46% and A: 3.12 ± 0.29 mV,9.29%) compared with the elderly subjects (T: 2.78 ± 0.33 mV, 11.98%).The most important finding is that the linear slope coefficient for the EMG (amplitude) as a function of time for the muscle of the ‘V’ group (r2=0.591,P0.05) and ‘A’ groups (r2=0.203, P > 0.05).The results obtained in this study can be used to improve the current understanding of the mechanics and muscle functions of the BB muscle of individuals from different age groups during isometric contraction

PCA and deep learning based myoelectric grasping control of a prosthetic hand

Background For the functional control of prosthetic hand, it is insufficient to obtain only the motion pattern information. As far as practicality is concerned, the control of the prosthetic hand force is indispensable. The application value of prosthetic hand will be greatly improved if the stable grip of prosthetic hand can be achieved. To address this problem, in this study, a bio-signal control method for grasping control of a prosthetic hand is proposed to improve patient’s sense of using prosthetic hand and the thus improving the quality of life. Methods A MYO gesture control armband is used to collect the surface electromyographic (sEMG) signals from the upper limb. The overlapping sliding window scheme are applied for data segmentation and the correlated features are extracted from each segmented data. Principal component analysis (PCA) methods are then deployed for dimension reduction. Deep neural network is used to generate sEMG-force regression model for force prediction at different levels. The predicted force values are input to a fuzzy controller for the grasping control of a prosthetic hand. A vibration feedback device is used to feed grasping force value back to patient’s arm to improve patient’s sense of using prosthetic hand and realize accurate grasping. To test the effectiveness of the scheme, 15 able-bodied subjects participated in the experiments. Results The classification results indicated that 8-channel sEMG applying all four time-domain features, with PCA reduction from 32 to 8 dimensions results in the highest classification accuracy. Based on the experimental results from 15 participants, the average recognition rate is over 95%. On the other hand, from the statistical results of standard deviation, the between-subject variations ranges from 3.58 to 1.25%, proving that the robustness and stability of the proposed approach. Conclusions The method proposed hereto control grasping power through the patient’s own sEMG signal, which achieves a high recognition rate to improve the success rate of grip and increases the sense of operation and also brings the gospel for upper extremity amputation patients

The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human-Computer Interface

http://doi.org/10.4018/978-1-4666-6090-8 ISBN 13 : 9781466660908 EISBN13: 9781466660915International audienceMuscle-computer interfaces (MCIs) based on surface electromyography (EMG) pattern recognition have been developed based on two consecutive components: feature extraction and classification algorithms. Many features and classifiers are proposed and evaluated, which yield the high classification accuracy and the high number of discriminated motions under a single-session experimental condition. However, there are many limitations to use MCIs in the real-world contexts, such as the robustness over time, noise, or low-level EMG activities. Although the selection of the suitable robust features can solve such problems, EMG pattern recognition has to design and train for a particular individual user to reach high accuracy. Due to different body compositions across users, a feasibility to use anthropometric variables to calibrate EMG recognition system automatically/semi-automatically is proposed. This chapter presents the relationships between robust features extracted from actions associated with surface EMG signals and twelve related anthropometric variables. The strong and significant associations presented in this chapter could benefit a further design of the MCIs based on EMG pattern recognition

Detection of Swallowing Events to Quantify Fluid Intake in Older Adults Based on Wearable Sensors

The percentage of adults aged 65 an over, defined as older adults, is prospected to increase in the coming decades over the total population. With such an increase, it is essential that healthcare technologies evolve to cater for the needs of an aging population. One such need is hydration: due to both physiological and psychological reasons, older adults tend to be more prone to develop dehydration, which in turn increases the chances of morbidity and mortality. Currently, there are no gold standards to monitor hydration, with most methods relying on filling manual forms. Thus, there is an urgent need to develop techniques which can accurately monitor fluid intake and prevent dehydration in older adult, especially those residing in healthcare settings. Several methods, such as smart cups and on-body sensors such as microphones, were proposed in the literature, however none of these have been widely investigated, and often the presented results were based on an extremely small cohort. Therefore, the scope of this PhD project is to investigate and develop methods that can detect swallowing events and that can quantify the volume of ingested fluids by leveraging on signals harvested using non-invasive, on-body sensors. Two types of on-body sensors were selected and used throughout this research: namely surface Electromyographic sensors (sEMG) and microphones. These sensors were then used to collect sound and electric signals from the subjects while swallowing boluses of different viscosities and while performing actions not related to swallowing but that could recruit the same muscles or produce similar sounds, such as talking or coughing. Features were then extracted from the collected observations and used to train Machine Learning (ML) and Deep Learning (DL) models to analyse their ability to differentiate between swallowing and non-swallowing actions, to distinguish between different bolus types, and to quantify the volume of fluid ingested. Results showed a precision of 81.55±3.40% in differentiating between swallows and non-swallows and a precision of 81.74±8.01% in distinguishing between bolus types, both given by the sEMG. Also, a root mean square error (RMSE) of 3.94±1.31 ml in estimating fluid intake was obtained using the microphone. The significance of the findings exposed in this thesis rely on the fact that surface EMGs and microphones demonstrate a significant potential in fluid intake monitoring, and on the concrete possibility of developing a non-invasive, reliable system that could prevent dehydration in older adults living in healthcare settings

Evaluación no invasiva del impulso neural respiratorio y su relación con la respuesta mecánica mediante el análisis de señales electromiográficas de músculos respiratorios

Respiratory muscle contraction occurs in response to the electrical stimulation of the muscles. These electrical stimuli originate in the respiratory neurons of the brainstem, are transmitted via motor nerves to the neuromuscular junctions and propagate along muscle fibers. Respiratory electromyography measures the electrical activity of respiratory muscles in response to this nerve stimulation. The neural respiratory drive (NRD) is best expressed in a phrenic neurogram, but this is not feasible in humans. Alternatively, measurements of the diaphragm electromyographic signal (EMGdi) would most likely reflect phrenic neurogram activity. EMGdi signal can be recorded using invasive methods, involving the use of needle electrodes or electrodes positioned in the esophagus at the level of the diaphragm. As a non-invasive alternative, the study of respiratory muscle activity can be addressed by surface electromyography. The onset and offset of the neural inspiratory time (nton and ntoff, respectively) are fundamentally important measurements in studies of patient-ventilator interaction, where the level of assistance delivered by the ventilator is controlled by patient demand. Cardiac artifacts (ECG) often make it difficult to utilize EMGdi. To overcome the shortcoming of the ECG, in this thesis is proposed to use sample entropy with fixed tolerance values (fSampEn), a robust technique against impulsive noise. To evaluate nton and ntoff estimation it has been carried out an experimental study with surface EMGdi signals recorded in healthy subjects during two respiratory protocols designed to evaluate the influence of different breathing patterns on the EMGdi. These protocols consisted of a stepwise increase in respiratory rate (RR) with constant fractional inspiratory time (Ti/Ttot) and a stepwise decrement in the Ti/Ttot with constant RR, respectively. The developed algorithms allowed to determine the nton and ntoff and derive the RR, Ti and Ti/Ttot neural ventilatory parameters. The EMGdi amplitude provides a real-time indirect measure of the NRD, which reflects the load on the respiratory muscles. The NRD, assessed by normalized EMGdi signals, is higher in patients with respiratory disease than in healthy subjects. To evaluate the behavior of the fSamp En, as a method for improving the measurement of NRD from EMGdi signals in the presence of cardiac activity, compared to the average rectified value and root mean square value approaches, first, these methods have been applied to synthetic EMGdi signals . Secondly, we tested the proposed methods in an experimental study with EMGdi signals recorded in healthy subjects during an incremental inspiratory load test. The EMGdi amplitude allowed to evaluate changes in the respiratory muscle activation patterns and estimate the NRD. Also, this thesis contributes to the study of the respiratory activity by the non-invasive recording of mechanomyographic low frequency (BF) activity in healthy subjects and in patients with chronic obstructive pulmonary disease, allowing the study of bilateral asynchrony of the diaphragm and the RR. Finally, we have proposed the use of concentric ring electrodes as an alternative to improve the spatial resolution of electromyographic recordings, and eliminate the problems associated with the location and orientation of the bipolar configuration. The approaches presented in this doctoral thesis based on the analysis of electromyographic and mechanomyographic signals of respiratory museles allow to extract complementary information to current use techniques of and contribute to the study of respiratory function in the clinical setting .La contracción de los músculos respiratorios se produce en respuesta a la estimulación eléctrica. Estos estímulos se originan en las neuronas respiratorias del tronco del encéfalo, se transmiten a través de los nervios motores a las uniones neuromusculares y se propagan a lo largo de las fibras musculares. La electromiografía respiratoria mide la actividad eléctrica de los músculos respiratorios en respuesta a esta estimulación nerviosa. El impulso neural respiratorio (NRD) se expresa mejor a través del neurograma frénico, pero esto no es factible en los seres humanos. Como alternativa, la medida de la señal electromiográfica del diafragma (EMGdi) refleja de forma indirecta la actividad frénica. La señal EMGdi puede registrarse utilizando métodos invasivos, lo que implica el uso de electrodos de aguja o electrodos colocados en el esófago a nivel del diafragma . Como alternativa no invasiva, el estudio de la actividad muscular respiratoria puede abordarse mediante la electromiografía de superficie. El inicio y fin del tiempo neural inspiratorio (nton y ntoff, respectivamente) son medidas de importancia en los estudios de interacción paciente-ventilador, donde el nivel de la asistencia proporcionada por el ventilador es controlado por la demanda del paciente. Los artefactos cardíacos (ECG) a menudo hacen que sea difícil de utilizar la señal EMGdi. Para superar el inconveniente de la interferencia ECG, en la presente tesis se propone utilizar la entropía muestra! con valores de tolerancia fijos (fSampEn), una técnica que es robusta contra el ruido de tipo impulsivo. Para evaluar la estimación del nton y ntoff se ha realizado un estudio experimental con señales EMGdi superficie registrada en sujetos sanos durante dos protocolos respiratorios, diseñados para evaluar la influencia de los diferentes patrones respiratorios sobre la señal EMGdi. Estos protocolos consistieron en un aumento gradual de la frecuencia respiratoria (RR) con un tiempo inspiratorio (Ti) fracciona! constante (Ti!Ttot) y en una disminución gradual en el Ti!Ttot con una RR constante, respectivamente. Los algoritmos desarrollados han permitido determinar el nton y el ntoff y derivar los parámetros ventilatorios RR, Ti, y TifTtot neurales. La amplitud de la EMGdi proporciona una medida indirecta del NRD, que refleja la carga sobre los músculos respiratorios. El NRD, evaluado en señales EMGdi normalizadas, es mayor en pacientes con enfermedades respiratorias que en sujetos sanos. Para evaluar el comportamiento de la fSampEn, como un método para mejorar la medición del NRD a partir de señales EMGdi en presencia de ECG, en comparación con los enfoques basados en el uso del valor rectificado medio y valor cuadrático medio, primero, se han aplicado estos métodos a señales EMGdi sintéticas . En segundo lugar, hemos probado los métodos propuestos en un estudio experimental con señales EMGdi registradas en sujetos sanos durante una prueba de carga inspiratoria incremental. La amplitud de la EMGdi permitió evaluar los cambios en el patrón de activación de los músculos respiratorios y estimar el NRO. Asimismo, esta tesis doctoral contribuye al estudio de la actividad respiratoria mediante el registro no invasivo de actividad mecanomiográfica de baja frecuencia (BF) en sujetos sanos y en pacientes con enfermedad obstructiva crónica, permitiendo el estudio de la asincronía bilateral del diafragma y la RR. Finalmente, hemos propuesto el uso de electrodos de anillos concéntricos como una alternativa para mejorar la resolución espacial de los registros electromiográficos, y eliminar los problemas asociados a la localización y orientación de la configuración bipolar. Los enfoques presentados en esta tesis doctoral basados en el análisis de señales electromiográficas y mecanomiográficas de los músculo