Sharif Digital Repository

The aim of a brain-computer interface (BCI) system is to establish a new communication system that translates human intentions, reflected by measures of brain signals such as magnetoencephalogram (MEG), into a control signal for an output device. In this paper, an algorithm is proposed for discriminating MEG signals, which were recorded during hand movements in four directions. These signals were presented as data set 3 of BCI competition IV. The proposed algorithm has four main stages: pre-processing, primary feature extraction, the selection of efficient features, and classification. The classification stage was a combination of linear SVM and linear discriminant analysis classifiers. The... 

Gesture and motion recognition are needed for a variety of applications. The use of human hand motions as a natural interface tool has motivated researchers to conduct research in the modeling, analysis and recognition of various hand movements. In this work, we introduce a 3-D hand motion recognition. We use 3-D landmarked points on finger tips and joints followed by a HMM (Hidden Markov Model) to recognize hand motions. Experimentally, in an effort to evaluate the formation of hand gestures similar to those used in rehabilitation sessions, we studied three hand motions. Using natural hand motion features in an uncontrolled environment, we were able to classify, differentiate and quantify... 

This article, introduces a new haptic robot, wrist-RoboHab, for upper limb rehabilitation of post stroke, orthopedic and Parkinson patients., The robot is designed for hand movement therapy and could be used for both treatment and evaluation purposes in three operational states; forearm supination/pronation, wrist flexion/extension and ulnar/radial deviation. At first the mechanical design and control system are described. Then the results of a case study are demonstrated. Clinical results, showed an improvement in Fugle-Meyer, AROM, power and the biomechanical assessment of the spasticity in a chronic patient. Furthermore, it was approved that the robot can have a good interaction with... 

The use of human hand gestures as a natural interface tool has motivated researchers to conduct research in the modeling, analyzing and recognition of various hand movements. In particular, human computer intelligent interaction has been a focus for research in vision-based gesture recognition. In this work, we introduce a 3D hand model reconstruction method which offers flexible and elaborate representation of hand gestures. We used 20 landmarked points on tips and joints of the fingers and calculated the 3D coordinates of these points through a stereo vision system. Our results show that such reconstruction provides a precise 3D hand model only to be influenced by intrinsic and extrinsic... 

Affordable motion sensors that are recently developed for video gaming have formed a budding line of research in the field of physical rehabilitation. These sensors have been used in many task-based applications to analyze the patients' status based on their completion of assigned tasks. However, as the accuracy of such sensors is lower than that of the clinical ones, their measured data has had very limited use in quantitative motion analysis to this date. The aim of this article is to determine Kinect's ability and accuracy in calculating higher-order kinematic parameters, such as velocity and acceleration, in hand movements. Four methods, i.e. moving average, Butterworth filter, B-spline,... 

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... 

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 (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... 

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...