Estimation of individual muscle force and its contribution to a joint moment has an important role in understanding injuries and disease. In previous work, we have developed EMG-driven models to estimate isometric forces in muscles about the elbow (Manal et al., 2002). Similarly, Lloyd and Besier (2003) used EMG-driven models to predict dynamic flexion and extension knee moments for a varied ra...

In this article will be presented a novel way to record and filter the Electromyogram (EMG) signal. EMG signals are generated when the muscles activates. In our case the user’s eye muscle movements in any direction will be recorded and filtered, so we will be able to observe when the user looks with his/her eyes up, down, left or right. In this article we use the non-invasive EMG signal recordi...

Electromyogram signals (EMGs) contain valuable information that can be used in man-machine interfacing between human users and myoelectric prosthetic devices. However, EMG signals are complicated and prove difficult to analyze due to physiological noise and other issues. Computational intelligence and machine learning techniques, such as artificial neural networks (ANNs), serve as powerful tool...

This paper proposes a pattern classi"cation method of time-series EMG signals for prosthetic control. To achieve successful classi"cation for non-stationary EMG signals, a new neural network structure that combines a common back-propagation neural network with recurrent neural "lters is used. A convergence time of the network learning can be regulated by a new learning method based on dynamics ...

Researchers and clinicians routinely rely on interference electromyograms (EMGs) to estimate muscle forces and command signals in the neuromuscular system (e.g., amplitude, timing, and frequency content). The amplitude cancellation intrinsic to interference EMG, however, raises important questions about how to optimize these estimates. For example, what should the length of the epoch (time wind...

This paper discusses the conversion of surface electromyography signals (s-EMG) to torque for robotic rehabilitation. Genetic algorithm (GA) has been applied as control algorithm for a number of selected mathematical models. s-EMG signals was treated as input to the mathematical model where pararameters of the mathematicl model were optimized using GA. Hence, the estimated torque is considered ...

We introduce a new method to perform a blind deconvolution of the surface electromyogram (EMG) signals generated by isometric muscle contractions. The method extracts the information from the raw EMG signals detected only on the skin surface, enabling longtime noninvasive monitoring of the electromuscular properties. Its preliminary results show that surface EMG signals can be used to determine...

In this manuscript, we present two 'Bionic Clicker' systems, the first designed to demonstrate electromyography (EMG) based control systems for educational purposes and the second for research purposes. EMG based control systems pick up electrical signals generated by muscle activation and use these as inputs for controllers. EMG controllers are widely used in prosthetics to control limbs. The ...

OBJECTIVE This paper proposes a novel method for the extraction and classification of individual motor unit action potentials (MUAPs) from intramuscular electromyographic signals. METHODOLOGY The proposed method automatically detects the number of template MUAP clusters and classifies them into normal, neuropathic or myopathic. It consists of three steps: (i) preprocessing of electromyogram (...

This paper presents the investigation pattern of arm movements for the purpose of the rehabilitation for a stroke patient in the virtual reality (VR) environments. The investigation results are used to design the virtual objects in the virtual environments. The muscle activities are analyzed by using electromyography (EMG). Six EMG channel are attached to the right arm of the subject, which is ...