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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CitedBy_id crossref_primary_10_1016_j_measurement_2024_114186
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Cites_doi 10.1109/ACCESS.2021.3094243
10.3390/e23030328
10.1016/j.eswa.2020.114226
10.1016/j.ins.2020.05.070
10.1109/JSEN.2020.2967100
10.5373/JARDCS/V12SP7/20202102
10.3390/s18103363
10.1007/s11042-020-10304-x
10.3390/electronics9111831
10.1007/s10489-020-01893-z
10.3390/jsan10030039
10.30534/ijeter/2020/28832020
10.1109/ACCESS.2021.3114871
10.3390/s18093153
10.1109/ACCESS.2021.3061626
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References Ramirez (ref10) 2021; 9
Villaverde (ref13) 2020; 9
Sreenidhi (ref3) 2020; 8
Deng (ref17) 2018; 18
Khan (ref6) 2021; 9
Torti (ref7) 2018
ref22
Mauldin (ref8) 2018; 18
ref21
Sangeetha (ref12) 2020; 12
ref16
Sultana (ref5) 2021; 23
Soni (ref14) 2020
Galvão (ref4) 2021; 168
Hashim (ref18) 2021; 51
Thakur (ref2) 2021; 10
Vaiyapuri (ref9) 2021; 9
Panda (ref20) 2021; 80
Bhattacharya (ref1) 2020; 20
Fathy (ref19) 2021; 14
Mrozek (ref11) 2020; 537
Nari (ref15) 2016
References_xml – start-page: 405
  year: 2018
  ident: ref7
  article-title: Embedded real-time fall detection with deep learning on wearable devices
– volume: 9
  start-page: 113879
  year: 2021
  ident: ref9
  article-title: Internet of things and deep learning enabled elderly fall detection model for smart homecare
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2021.3094243
– volume: 23
  start-page: 328
  year: 2021
  ident: ref5
  article-title: Classification of indoor human fall events using deep learning
  publication-title: Entropy
  doi: 10.3390/e23030328
– volume: 168
  start-page: 114226
  year: 2021
  ident: ref4
  article-title: A multimodal approach using deep learning for fall detection
  publication-title: Expert Systems with Applications
  doi: 10.1016/j.eswa.2020.114226
– volume: 537
  start-page: 132
  year: 2020
  ident: ref11
  article-title: Fall detection in older adults with mobile IoT devices and machine learning in the cloud and on the edge
  publication-title: Information Sciences
  doi: 10.1016/j.ins.2020.05.070
– volume: 20
  start-page: 5072
  year: 2020
  ident: ref1
  article-title: Deep learning radar design for breathing and fall detection
  publication-title: IEEE Sensors Journal
  doi: 10.1109/JSEN.2020.2967100
– volume: 14
  start-page: 1
  year: 2021
  ident: ref19
  article-title: Archimedes optimization algorithm based maximum power point tracker for wind energy generation system
  publication-title: Ain Shams Engineering Journal
– volume: 12
  start-page: 232
  year: 2020
  ident: ref12
  article-title: Fall detection for elderly people using video-based analysis
  publication-title: Journal of Advanced Research in Dynamical and Control Systems
  doi: 10.5373/JARDCS/V12SP7/20202102
– ident: ref21
– volume: 18
  start-page: 3363
  year: 2018
  ident: ref8
  article-title: SmartFall: A smartwatch-based fall detection system using deep learning
  publication-title: Sensors
  doi: 10.3390/s18103363
– volume: 80
  year: 2021
  ident: ref20
  article-title: Oppositional salp swarm algorithm with mutation operator for global optimization and application in training higher order neural networks
  publication-title: Multimedia Tools and Applications
  doi: 10.1007/s11042-020-10304-x
– ident: ref22
– volume: 9
  start-page: 1831
  year: 2020
  ident: ref13
  article-title: A simulator to support machine learning-based wearable fall detection systems
  publication-title: Electronics
  doi: 10.3390/electronics9111831
– volume: 51
  start-page: 1531
  year: 2021
  ident: ref18
  article-title: Archimedes optimization algorithm: A new metaheuristic algorithm for solving optimization problems
  publication-title: Applied Intelligence
  doi: 10.1007/s10489-020-01893-z
– start-page: 229
  year: 2020
  ident: ref14
  article-title: An approach to enhance fall detection using machine learning classifier
– volume: 10
  start-page: 39
  year: 2021
  ident: ref2
  article-title: A study of fall detection in assisted living: Identifying and improving the optimal machine learning method
  publication-title: JSAN
  doi: 10.3390/jsan10030039
– volume: 8
  start-page: 780
  year: 2020
  ident: ref3
  article-title: Real-time human fall detection and emotion recognition using embedded device and deep learning
  publication-title: International Journal of Emerging Trends in Engineering Research
  doi: 10.30534/ijeter/2020/28832020
– volume: 9
  start-page: 131614
  year: 2021
  ident: ref6
  article-title: 3D hand gestures segmentation and optimized classification using deep learning
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2021.3114871
– start-page: 88
  year: 2016
  ident: ref15
  article-title: A simple design of wearable device for fall detection with accelerometer and gyroscope
– ident: ref16
– volume: 18
  start-page: 3153
  year: 2018
  ident: ref17
  article-title: Hyperspectral image classification with capsule network using limited training samples
  publication-title: Sensors
  doi: 10.3390/s18093153
– volume: 9
  start-page: 33532
  year: 2021
  ident: ref10
  article-title: Fall detection and activity recognition using human skeleton features
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2021.3061626
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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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