This paper proposes robust methods for processing EMG (electromyography) signals in the framework of audio-EMGbased speech recognition. The EMG signals are captured when uttered and used as auxiliary information for recognizing speech. Two robust methods (Cepstral Mean Normalization and Spectral Subtraction) for EMG signal processing are investigated to improve the recognition performance. We a...

EMG signals are an image of the control from the central nervous system transmitted to muscles. Intramuscular EMG signals are collected directly in muscles. The collected data contain information on the neural control of the muscle. This information can be used for controlling external devices (myoelectric control), however realtime processing of intramuscular EMG signals is complex. The aim of...

Modern components and materials in combination with recent pattern recognition methods for electromyographic (EMG) signals enable creating multi-functional arm prostheses with intelligent and user-friendly control [1]. While the usage of pattern recognition of features extracted from EMG signals has proven highly efficient in transradial prostheses [2,3], most current transhumeral prostheses ut...

Emotional arousal can cause changes in respiration pattern and muscle stress in the vocal tract during speech production. Many researchers confirm that during muscular fatigue, there is a shift of the power spectrum toward lower frequencies and an increase in the root mean square amplitude of the EMG signal. In this paper, investigations were carried out analyze the effect of fatigue on speech ...

EMG signals are electromyogram signals generated by firing of MUs (motor units) in muscle fibers. The decomposition of EMG signal of a muscle provides useful information for the diagnosis of neuro-muscular diseases by physician and neurologist. In decomposition of EMG signal different MUAPs (Motor Unit Action Potentials) are classified into different categories. This paper gives a review of dif...

1,3 Electronics and Communication Engineering, Swami Keshwanand Institute of Technology, Jaipur,(India) 2 Electronics and Communication Engineering, Sri Balaji College of Engineering & Technology, Jaipur,(India) ABSTRACT An approach for removal of the presence of ECG in electromyographic signals by means of time-variant harmonic modeling of the cardiac artifact. The amplitude and frequency time...

Advances in bioengineering have led to increasingly sophisticated prosthetic devices for amputees and paralyzed individuals. Control of such devices necessitates real-time classification of biosignals, e.g., electromyographic (EMG) signals recorded from intact muscles. In this paper, we show that a 4-degrees-of-freedom robotic arm can be controlled in real-time using non-invasive surface EMG si...

The motor unit action potentials (MUAPs) in an electromyographic (EMG) signal provide a significant source of information for the assessment of neuromuscular disorders. In this work, different types of machine learning methods were used to classify EMG signals and compared in relation to their accuracy in classification of EMG signals. The models automatically classify the EMG signals into norm...

Robot arms are versatile tools found in a wide range of applications. While the user moves his arm, (EMG) activity is recorded from selected muscles, using surface EMG electrodes. By a decoding procedure the muscular activity is transformed to kinematic variables that are used to control the robot arm. EMG signals have been used as control signals for robotics devices in the past. EMG signals, ...