Optimizing Artificial Neural Network Performance Using Gray Level Co-occurrence Matrix (GLCM) Feature Extraction for Monitoring Elderly Movement

Abstract

Machine learning methods are used to detect elderly accidents while image processing is used to support machine learning performance so that detection performance can be better. This study used two methods used simultaneously, namely GLCM and ANN. The study consisted of preparation of human gesture datasets, preprocessing stage, application of GLCM, analysis of feature extraction results, classification using ANN and analysis of motion class detection results. Overall, the GLCM method with homogeneity parameters and ANN as a classifier obtained an accuracy of 24.32%. The GLCM method with contrast parameters and ANN as a classifier gets an accuracy of 99.84%. The GLCM method with mean parameters and ANN as the classifier gets an accuracy of 99.99%. The GLCM method with dissimilarity parameters and ANN as a classifier to classify hand movement images gets the best accuracy of 100%.