Electromyography (EMG) is very important to capture muscle activity. Although many jobs establish data acquisition system, however, it is also essential to demonstrate that these data are reliable. In this sense, one proposes a design and implementation of a data acquisition system with the Myoware device and the ATmega329P microcontroller. One also proved its reliability by classifying the movement of the fingers of the hand, with the help of the algorithm k-Nearest Neighbors (KNN) and the application of Classification Learner code of Matlab. The results show a success rate of 99.1%.
Abstract-In the present project it is focused on patients Amotrophy Lateral Sclerosis (ALS), so these patients do not have control of their motor functions therefore are unable to move on their own, requiring third party assistance to move with his wheelchair. Patients of Amotrophy Lateral Sclerosis do not lose their cognitive ability, which is why you can use it to control his wheelchair as part of a computer chandler system using Cyton board of open BCI brain signals is extracted and with the help of deep learning classification of signals, so the patient can move their own means be held. In this project it was possible to perform communication computer brain, in addition to the proper functioning of the system, in addition it was possible to implement a security system that protects the patient against accidents, so the patient is safer to move; whole system gives the patient, partial independence and thus improve their quality of life.
At the beginning of the year 2017, different regions of Peru suffered from heavy rains mainly due to the 'El Niño' and 'La Niña' phenomena. As a result of these massive storms, several cities were affected by overflows and landslides. Chosica and Piura were the most affected cities. On the other hand, the satellite images have many applications, one of them is the aid for the better management of the natural disasters (post-disaster management). In this sense, the present work proposes the use of radar satellite images from Sentinel constellation to make an analysis of the most-affected areas by floods in the cities of Chosica and Piura. The applied methodology is to analyse and compare two images (one before and one after the disaster) to identify the affected areas based on differences between both images. The analysing process includes radiometric calibration, speckle filtering, terrain correction, histogram plotting, and image binarization. The results show maps of the analysed cities and identify a significant number of areas flooded according to satellite images from March 2017. Using the resulting maps, authorities can make better decisions. The satellite images used were from the Sentinel 1 satellite belonging to the European Union.
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