Sharif Digital Repository

Surface electromyogram (sEMG) signals are extensively used for rehabilitation and control purposes. However due to their intrinsic complexities and intense sensor crosstalk, feature classification and pattern recognition of sEMG signals especially for motion analysis are quite challenging. This study proposes a versatile sEMG Artificial Neural Network based regression approach to evaluate a simple elbow motion with respect to a reference frame. The proposed approach attempts to appropriately interpolate intermediate position angles in an attempt to evaluate and substantiate a continuous motion of the forearm. Results show that based on the proposed algorithm, with a correlation of about 91%... 

In this paper, a novel moving object modeling suitable for multicamera correspondence is introduced. Taking into consideration the color and motion features of foreground objects in each independent video stream, our method segments the existing moving objects and constructs a graph-based structure to maintain the relational information of each segment. Using such graph structures reduces our correspondence problem to a subgraph optimal isomorphism problem. The proposed method is robust against various resolutions and orientations of objects at each view. Our system uses the fuzzy logic to employ a human-like color perception in its decision making stage in order to handle color inconstancy...