6,178 research outputs found

    Stand-alone wearable system for ubiquitous real-time monitoring of muscle activation potentials

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    Wearable technology is attracting most attention in healthcare for the acquisition of physiological signals. We propose a stand-alone wearable surface ElectroMyoGraphy (sEMG) system for monitoring the muscle activity in real time. With respect to other wearable sEMG devices, the proposed system includes circuits for detecting the muscle activation potentials and it embeds the complete real-time data processing, without using any external device. The system is optimized with respect to power consumption, with a measured battery life that allows for monitoring the activity during the day. Thanks to its compactness and energy autonomy, it can be used outdoor and it provides a pathway to valuable diagnostic data sets for patients during their own day-life. Our system has performances that are comparable to state-of-art wired equipment in the detection of muscle contractions with the advantage of being wearable, compact, and ubiquitous

    Repeatability of innervation zone identification in the external anal sphincter muscle

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    Knowledge of the distribution of the innervation zones (IZs) of the external anal sphincter (EAS) may be useful for preventing anal sphincter incompetence during vaginal delivery. A method proposed for the automatic estimation of the distribution of IZs of EAS from high-density surface electromyography (EMG) was evaluated for repeatability in continent volunteers. Methods: In 13 healthy female subjects (age: 35 11 years) surface EMG signals were acquired using an anal probe with three circumferential electrode arrays (of 16 contacts each) at different depths within the anal canal (15mm distance between the centers of adjacent arrays), during four independent experimental sessions. Three maximal voluntary contractions (MVCs) of 10 sec were performed for each session for a total of 12 contractions per subject. Repeatability of the estimation of the distribution of IZ was tested by evaluating the coefficient of multiple correlations (CMC) between the IZ distributions estimated from the signals recorded from each subject. Results: A high repeatability (CMC > 0.8) was found comparing IZ distributions estimated from signals recorded by each array within the same session. A slightly lower value was obtained considering signals recorded during different sessions (CMC > 0.7), but a higher value (CMC > 0.8) was obtained after aligning the estimated IZ distributions. The realignment compensates for the operator's error in repositioning the probe in the same position during different sessions. Conclusion: This result justifies clinical studies using high-density surface EMG in routine examinations, providing information about IZs of EAS and assessing the possibilities of preventing neuronal trauma during vaginal delivery

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

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    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

    Emotion Detection Using Noninvasive Low Cost Sensors

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    Emotion recognition from biometrics is relevant to a wide range of application domains, including healthcare. Existing approaches usually adopt multi-electrodes sensors that could be expensive or uncomfortable to be used in real-life situations. In this study, we investigate whether we can reliably recognize high vs. low emotional valence and arousal by relying on noninvasive low cost EEG, EMG, and GSR sensors. We report the results of an empirical study involving 19 subjects. We achieve state-of-the- art classification performance for both valence and arousal even in a cross-subject classification setting, which eliminates the need for individual training and tuning of classification models.Comment: To appear in Proceedings of ACII 2017, the Seventh International Conference on Affective Computing and Intelligent Interaction, San Antonio, TX, USA, Oct. 23-26, 201

    Monitoring muscle fatigue following continuous load changes

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    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

    Electromyography - A Reliable Technique for Muscle Activity Assessment

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    In recent years, many questions have been raised on the credibility of Electromyography (EMG) as a technique to evaluate muscle activity, particularly by sports and fitness community. This questioning goes farther when it comes to surface electromyography (sEMG). This paper covers an overview of EMG, addresses some basic concepts and provide rudiment for research. Muscle activity assessment through EMG has been reviewed in terms of the type of movements. There are few limitations to EMG but these confines are addressable. The problem rather lies in the interpretation and generalization of that data. Limitations are there in every technology, precautionary measures must be taken to avoid those while using it. Reservations about EMG have been summarized along with their responses. A few techniques to analyze EMG data, and possibilities to extrapolate and interpret, are also provided. Current perspectives and practical applications of EMG and sEMG are also part of this article
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