IMPULSE CONTROL DISORDER RECOGNITION SYSTEM

Abstract

The aim of this project is for the machine to be able to analyse, monitor, and alert Impulse Control Disorder behaviour in order to prevent people from doing them subconsciously. This article examines the various methods for recognising human expressions and movement. Various levels of image processing, such as pre processing, segmentation, and attribute extraction, will be analysed. The pre processing level, segmentation, and feature extraction are all stages of image processing that will be analysed and addressed. Finally, the algorithms' output will be written in Python. To design the algorithm, this research uses the Artificial Convolutional Neural Network method. The key benefit ofthis method is that it allows for the extraction and identification of features that are beneficial for gesture recognition. Different neural network models are debated, with the Error-back propagation algorithm being used because of its ability to construct internal representations of features in classification. A suitable set of training parameters is specified and a network configuration is generated after trials and errors. The system begins by performing a pre-processing of the captured signal, which includes thresholding, inverting, and smoothing. The method also includes filtering, segmentation, resizing, and feature extraction. The feed forward method is then used to generate an output matrix across the network. The recognised gesture can be calculated using the output matrix. This device is intended to personalise the network for each user. The report also includes recommendations for future progress and findings.