Sharif Digital Repository

Estimation of muscle force is needed for monitoring or control purposes in many studies and applications that include direct human involvement such as control of prosthetic arms and human-robot interaction. A new model is introduced to estimate the force of muscle from the EMG signals. Estimation is based on Hammerstein-Wiener Model which consists of three blocks. These blocks are used to describe the nonlinearity of input and output and linear behavior of the model. The nonlinear network is designed base on the sigmoid network. The introduced model is trained by some data sets which are recorded from different people and tested by some other data sets. The simulation results show low error... 

Disability and motor disorders are some of the problems that people may face due to some factors such as accident, spinal cord injuries, brain damage, and the presence of a problem in neurological commands due to stroke. One of the proposed methods for solving the problems of these people is the use of exoskeletons to generate movement. The main goal of this paper is the control of Sharif Exoskeleton Robot and the reduction of interaction force between user and robot by using human intent detection. This goal is done by estimating the knee torque of one degree of freedom in a swing phase using Hill model based on the EMG data from the thigh muscles. Accordingly, the calibration algorithm is... 

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 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... 

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...