A deep learning approach for detecting tic disorder using wireless channel information

Wireless signal technology performs a key role in the research area of medical science to detect diseases that are associated with the human gesture. Recently, wireless channel information (WCI) has received vast consideration because of its potential practice of detecting the human behavior. In this article, we present the convolutional neural network (CNN) model to classify WCI-based image data and determine the involuntary movement (tic disorder) diseases. Motor and vocal are two aspects of tic disorder and depend on the amount of complication, both aspects classified into the simple and complex group, and each group has several symptoms. Using WCI data of symptoms from the simple and complex group of motor aspects, we form a dataset to train the CNN model. Experimental results show that CNN provides satisfying result in classification, and accuracy is more than 97%.