TY - JOUR
T1 - Computer Vision with Optimal Deep Stacked Autoencoder-based Fall Activity Recognition for Disabled Persons in the IoT Environment
AU - Alabdulkreem, Eatedal
AU - Marzouk, Radwa
AU - Alduhayyem, Mesfer
AU - Al-Hagery, Mohammed Abdullah
AU - Motwakel, Abdelwahed
AU - Hamza, Manar Ahmed
N1 - Publisher Copyright:
© 2023, King Salman Center for Disability Research. All rights reserved.
PY - 2023/9/15
Y1 - 2023/9/15
N2 - Remote monitoring of fall conditions or actions and the daily life of disabled victims is one of the indispensable purposes of contemporary telemedicine. Artificial intelligence and Internet of Things (IoT) techniques that include deep learning and machine learning methods are now implemented in the field of medicine for automating the detection process of diseased and abnormal cases. Many other applications exist that include the real-time detection of fall accidents in older patients. Owing to the articulated nature of human motion, it is unimportant to find human action with a higher level of accuracy for every application. Likewise, finding human activity is required to automate a system to monitor and find suspicious activities while executing surveillance. In this study, a new Computer Vision with Optimal Deep Stacked Autoencoder Fall Activity Recognition (CVDSAE-FAR) for disabled persons is designed. The presented CVDSAE-FAR technique aims to determine the occurrence of fall activity among disabled persons in the IoT environment. In this work, the densely connected networks model can be exploited for feature extraction purposes. Besides, the DSAE model receives the feature vectors and classifies the activities effectually. Lastly, the fruitfly optimization method can be used for the automated parameter tuning of the DSAE method which leads to enhanced recognition performance. The simulation result analysis of the CVDSAE-FAR approach is tested on a benchmark dataset. The extensive experimental results emphasized the supremacy of the CVDSAE-FAR method compared to recent approaches.
AB - Remote monitoring of fall conditions or actions and the daily life of disabled victims is one of the indispensable purposes of contemporary telemedicine. Artificial intelligence and Internet of Things (IoT) techniques that include deep learning and machine learning methods are now implemented in the field of medicine for automating the detection process of diseased and abnormal cases. Many other applications exist that include the real-time detection of fall accidents in older patients. Owing to the articulated nature of human motion, it is unimportant to find human action with a higher level of accuracy for every application. Likewise, finding human activity is required to automate a system to monitor and find suspicious activities while executing surveillance. In this study, a new Computer Vision with Optimal Deep Stacked Autoencoder Fall Activity Recognition (CVDSAE-FAR) for disabled persons is designed. The presented CVDSAE-FAR technique aims to determine the occurrence of fall activity among disabled persons in the IoT environment. In this work, the densely connected networks model can be exploited for feature extraction purposes. Besides, the DSAE model receives the feature vectors and classifies the activities effectually. Lastly, the fruitfly optimization method can be used for the automated parameter tuning of the DSAE method which leads to enhanced recognition performance. The simulation result analysis of the CVDSAE-FAR approach is tested on a benchmark dataset. The extensive experimental results emphasized the supremacy of the CVDSAE-FAR method compared to recent approaches.
KW - computer vision
KW - disabled persons
KW - fall activity
KW - human activity recognition
KW - Internet of Things
UR - http://www.scopus.com/inward/record.url?scp=105005434816&partnerID=8YFLogxK
U2 - 10.57197/JDR-2023-0044
DO - 10.57197/JDR-2023-0044
M3 - Article
AN - SCOPUS:105005434816
SN - 2676-2633
VL - 2
SP - 120
EP - 128
JO - Journal of Disability Research
JF - Journal of Disability Research
IS - 3
ER -