Sharif Digital Repository

Surface electromyogram (sEMG) signals, a noninvasive bioelectric signal, can be used for the rehabilitation and control of artificial extremities. Current sEMG pattern-recognition systems suffer from a limited number of patterns that are frequently intensified by the unsuitable accuracy of the instrumentation and analytical system. To solve these problems, we designed a multistep-based sEMG pattern-recognition system where, in each step, a stronger more capable relevant technique with a noticeable improved performance is employed. In this paper, we utilized the sEMG signals to classify and recognize six classes of hand movements. We employed an adaptive neurofuzzy inference system (ANFIS) to... 

Electromyogram signal (EMG) is an electrical manifestation of contractions of muscles. Surface EMG (sEMG) signal collected from the surface of skin has been used in diverse applications. One of its usages is in pattern recognition of hand prosthesis movements. The ability of current prosthesis devices has been generally limited to simple opening and closing tasks, minimizing their efficacy compared to natural hand capabilities. In order to extend the abilities and accuracy of prosthesis arm movements and performance, a novel sEMG pattern recognizing system is proposed. To extract more pertinent information we extracted sEMGs for selected hand movements. These features constitute our main... 

Background: Electromyography (EMG) is the study of muscle function through the inquiry of electrical signals that the muscles emanate. EMG signals collected from the surface of the skin (Surface Electromyogram: sEMG) can be used in different applications such as recognizing musculoskeletal neural based patterns intercepted for hand prosthesis movements. Current systems designed for controlling the prosthetic hands either have limited functions or can only be used to perform simple movements or use excessive amount of electrodes in order to achieve acceptable results. In an attempt to overcome these problems we have proposed an intelligent system to recognize hand movements and have provided... 

In recent years, electromyogram signal (EMG) feature selection, based on wavelet transform, has received considerable attention. This study introduces a new multiwavelet function for surface EMG (sEMG) signal intended for tasks that involve hand movement recognition. To create the new wavelet function, several types of well known mother wavelet were exploited and through their integration the proposed mother wavelet was generated. The proposed wavelet function closely reproduced the characteristics of the EMG signal, while increasing the recognition accuracy of hand movements. We used eight unique classes of hand motions and considered the ability of various mother wavelets and the proposed... 

Electromyogram signal (EMG) is an electrical manifestation of contractions of muscles. Surface EMG (sEMG) signal collected form surface of the skin has been used in diverse applications. One of its usages is exploiting it in a pattern recognition system which evaluates and synthesizes hand prosthesis movements. The ability of current prosthesis has been limited in simple opening and closing that decreases the efficacy of these devices in contrary to natural hand. In order to extend the ability and accuracy of prosthesis arm movements and performance, a novel approach for sEMG pattern recognizing system is proposed. In order to have a relevant comparison, present and recent research for... 

Surface Electromyogram signal collected from the surface of skin is a biopotential signal that may be influenced by different types of noise. This is a considerable drawback in the processing of the sEMG signals. To acquire the clean sEMG that contains useful information, we need to detect and eliminate these unwanted parts of signal. In this work, a new method based on wavelet thresholding technique is presented which provides an acceptable purified sEMG signal. sEMG signals for this study are extracted for various hand movements. We use three hand movements to calculate the near optimal estimation parameters. In this work two types of thresholding techniques, namely Stein unbiased risk... 

Electromyogram signal is a biopotential signal that may be measured on the surface of contracting muscles representing neuromuscular activities. This signal may be utilized in various applications such as clinical diagnosis of diseased neuromuscular systems and as a measurement tool for evaluation of rehabilitation activities. Another recent application is the usage of EMG signal in design and implementation of neural controlled prosthesis hands. For this purpose appropriate features of EMG signal are required such that intended hand movements may be recognized correctly. In this work we considered a new method based on quadratic time-frequency representation namely Wigner-Ville distribution... 

Electromyogram (EMG) signal is an electrical manifestation of muscle contractions. EMG signal collected from surface of the skin, a non-invasive bioelectric signal, can be used in different rehabilitation applications and artificial extremities control. This study has proposed to utilize the surface EMG (SEMG) signal to recognize patterns of hand prosthesis movements. It suggests using an adaptive neuro-fuzzy inference system (ANFIS) to identify motion commands for the control of a prosthetic hand. In this work a hybrid method for training fuzzy system, consisting of back-propagation (BP) and least mean square (LMS) is utilized. Also in order to optimize the number of fuzzy rules, a... 

It is well-known that the conventional reproducing kernel particle method (RKPM) is unfavorable when dealing with the derivative type essential boundary conditions [1-3]. To remedy this issue a group of meshless methods in which the derivatives of a function can be incorporated in the formulation of the corresponding interpolation operator will be discussed. Formulation of generalized moving least squares (GMLS) on a domain and GMLS on a finite set of points will be presented. The generalized RKPM will be introduced as the discretized form of GMLS on a domain. Another method that helps to deal with derivative type essential boundary conditions is the gradient RKPM which incorporates the...