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 fall events and save human lives. Generally, elderly people suffer from several diseases, and fall action is a common situation which can occur at any tim...

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Vydáno v:Computers, materials & continua Ročník 72; číslo 2; s. 2713 - 2727
Hlavní autoři: Saleh Alluhaidan, Ala, Alajmi, Masoud, N. Al-Wesabi, Fahd, Mustafa Hilal, Anwer, Ahmed Hamza, Manar, Motwakel, Abdelwahed
Médium: Journal Article
Jazyk:angličtina
Vydáno: Henderson Tech Science Press 2022
Témata:
Alarm systems
Algorithms
Deep learning
Fall detection
Feature extraction
Image enhancement
Image quality
Machine learning
Optimization
Optimization algorithms
Performance enhancement
Radial basis function
ISSN:1546-2226, 1546-2218, 1546-2226
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Abstract Human fall detection (FD) acts as an important part in creating sensor based alarm system, enabling physical therapists to minimize the effect of fall events and save human lives. Generally, elderly people suffer from several diseases, and fall action is a common situation which can occur at any time. In this view, this paper presents an Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection (IAOA-DLFD) model to identify the fall/non-fall events. The proposed IAOA-DLFD technique comprises different levels of pre-processing to improve the input image quality. Besides, the IAOA with Capsule Network based feature extractor is derived to produce an optimal set of feature vectors. In addition, the IAOA uses to significantly boost the overall FD performance by optimal choice of CapsNet hyperparameters. Lastly, radial basis function (RBF) network is applied for determining the proper class labels of the test images. To showcase the enhanced performance of the IAOA-DLFD technique, a wide range of experiments are executed and the outcomes stated the enhanced detection outcome of the IAOA-DLFD approach over the recent methods with the accuracy of 0.997.
AbstractList Human fall detection (FD) acts as an important part in creating sensor based alarm system, enabling physical therapists to minimize the effect of fall events and save human lives. Generally, elderly people suffer from several diseases, and fall action is a common situation which can occur at any time. In this view, this paper presents an Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection (IAOA-DLFD) model to identify the fall/non-fall events. The proposed IAOA-DLFD technique comprises different levels of pre-processing to improve the input image quality. Besides, the IAOA with Capsule Network based feature extractor is derived to produce an optimal set of feature vectors. In addition, the IAOA uses to significantly boost the overall FD performance by optimal choice of CapsNet hyperparameters. Lastly, radial basis function (RBF) network is applied for determining the proper class labels of the test images. To showcase the enhanced performance of the IAOA-DLFD technique, a wide range of experiments are executed and the outcomes stated the enhanced detection outcome of the IAOA-DLFD approach over the recent methods with the accuracy of 0.997.
Author Mustafa Hilal, Anwer
Saleh Alluhaidan, Ala
Ahmed Hamza, Manar
N. Al-Wesabi, Fahd
Motwakel, Abdelwahed
Alajmi, Masoud
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Snippet Human fall detection (FD) acts as an important part in creating sensor based alarm system, enabling physical therapists to minimize the effect of fall events...
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SubjectTerms Alarm systems
Algorithms
Deep learning
Fall detection
Feature extraction
Image enhancement
Image quality
Machine learning
Optimization
Optimization algorithms
Performance enhancement
Radial basis function
Title Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection System
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