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The motor unit profile, a representation of the trajectories of positive and negative turns of a scanning-EMG signal, is a new way to characterize the motor unit potential. Such characterization allows quantification of the scanning-EMG signal's complexity, which is closely related to the anatomy and physiology of the motor unit. To extract the motor unit profile, an algorithm that detects the turns of the scanning-EMG signal and links them using point-tracking techniques has been developed. The performance of this algorithm is sensitive to three parameters: the turn detection threshold, the maximum tracking interval threshold, and the trajectory purge threshold. Real scanning-EMG signals have been used to analyze the algorithm's behavior and the influence of the algorithm's parameters and to determine which parameter values provide the best performance. [ABSTRACT FROM AUTHOR]

A multistage procedure to detect eye features is presented. Multiresolution and topographic classification are used to detect the iris center. The eye corner is calculated combining valley detection and eyelid curve extraction. The algorithm is tested in the BioID database and in a proprietary database containing more than 1200 images. The results show that the suggested algorithm is robust and accurate. Regarding the iris center our method obtains the best average behavior for the BioID database compared to other available algorithms. Additional contributions are that our algorithm functions in real time and does not require complex post processing stages. [ABSTRACT FROM AUTHOR]