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.
Taking into account that in Peru, there is an increase in people with difficulties in speaking or communicating. According to the National Institute of Statistics and Informatics of Peru (INEI for its acronym in Spanish), around 80000 people use the gesturing language. For this reason, this research proposes to use the electromyography (EMG) signals to detect the hand movement and identify the alphabet of the sign language to provide essential communication to people who need it. The idea is to classify the signals and recognize the letters of the Spanish alphabet, interpreted in the Peruvian sign language. The results show the classification of the 27 letters of the alphabet with a general success rate of 93.9%.
Varicose veins also known as venous insufficiency, are dilated veins due to an accumulation of blood that occurs in different parts of the body, the most common are in the legs, in addition to having a higher index in women for clothing style that they use. Varicose veins are classified by grades ranging from I to IV and can cause pain, itching, cramps and even ulcers if they are treated in time. Not all varicose veins can be visible superficially, many of them begin inside of the skin. According to the WHO (World Health Organization) 10% of the world population has varicose veins. That is why the detection of suspicions of varicose veins in the legs was raised in this research work, first a thermal image will be obtained using the FLIR ONE Pro thermal camera following a necessary protocol of distance and temperature range. The thermal image is processed in MATLAB to identify the segments of the histogram of the thermal image, to obtain the area of the highest temperature indicating the presence of varicose vein in the subject's leg. The segmentation of the areas with the highest temperature was obtained as a result to be overlaid on the real image, showing the real image with the varicose vein segment found in the thermal image processing.
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