Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection System

Human fall detection (FD) acts as an important part in creating sensor based alarm system, enabling physical therapists to minimize the effect of events and save human lives. Generally, elderly people suffer from several diseases, action is a common situation which can occur at any time. In this view, paper presents Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection (IAOA-DLFD) model identify fall/non-fall events. The proposed IAOA-DLFD technique comprises different levels pre-processing improve input image quality. Besides, IAOA Capsule Network feature extractor derived produce optimal set vectors. addition, uses significantly boost overall FD performance by choice CapsNet hyperparameters. Lastly, radial basis function (RBF) network applied for determining proper class labels test images. To showcase enhanced technique, wide range experiments are executed outcomes stated outcome approach over recent methods accuracy 0.997.