Docker-Based Intelligent Fall Detection Using Edge-Fog Cloud Infrastructure

Divya, V.; Sri, R. Leena

doi:10.1109/jiot.2020.3042502

Cited by 31 publications

(17 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep Learning Image Processing. Deep convolutional neural networks enable complex training models of traditional machine learning algorithms [7]. The structure of the convolutional neural network is shown in Figure 1.…”

Section: Mutation Detection and Functional Study Inmentioning

confidence: 99%

“…The model is evaluated in terms of accuracy, and the strength of the fog layer is evaluated in terms of latency. Compared with the existing state-of-the-art algorithms available for detection, the accuracy of the proposed model is 98.5% [7]. It is of practical significance to study the mutation detection of congenital missing teeth by intelligent image detection.…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Mutation Detection and Functional Analysis of MSX1, PAX9, AXIN2, and BMP in Nonsyndromic Congenital Missing Teeth Based on Intelligent Image Detection

Yao

Zhang

Gao

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

Due to the complexity of clinical manifestations and the lack of standardized diagnostic criteria, it is still difficult to distinguish the etiological types of congenital edentulousness corresponding to genetic defects. This paper studies the application of deep learning image processing and digital image processing in medical images in detail and analyzes the functions of congenital edentulous hotspot genes. The cases in the control group and the study group were collected, and the gene mutations of direct sequence MSX1, PAX9, AXIN2, and BMP were analyzed, and new pathogens were found. The experimental results suggest that PAX9 and MSX1 genes may have a synergistic effect in nonsyndromic congenital edentulous patients. In severely missing teeth, the role of PAX9 may be greater than that of MSX1. The experimental results will help us lay the foundation for further understanding of the disease in the future.

show abstract

Section: Mutation Detection and Functional Study Inmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Mutation Detection and Functional Analysis of MSX1, PAX9, AXIN2, and BMP in Nonsyndromic Congenital Missing Teeth Based on Intelligent Image Detection

Yao

Zhang

Gao

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…By integrating both fog and DL, HAR applications can be handled more effectively. Instead, of sending the processing every time to the cloud, fog servers can handle them through the deployment of neural networks using Docker and Kubernetes 47 . To deploy the neural network in a fog node, docker can be used and managed 48 …”

Section: Related Workmentioning

confidence: 99%

“…Instead, of sending the processing every time to the cloud, fog servers can handle them through the deployment of neural networks using Docker and Kubernetes. 47 To deploy the neural network in a fog node, docker can be used and managed. 48 In Reference 27, the performance between DL and ML methods were compared for wrist accelerometer data and the DL method called convolutional neural network (CNN) have outperformed ML approaches such as random forest, support vector machine (SVM), and naive Bayes.…”

Section: Based Har Systemsmentioning

confidence: 99%

IHAR—A fog‐driven interpretable human activity recognition system

Shree¹,

Padmavathi²

2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Human activity recognition (HAR) is an active research area that is currently being applied to various healthcare applications such as fall detection, assisted living, etc. These applications make use of the Internet of Things which is widespread across today's world. One of the major challenges in these applications is the need for quick intelligent decisions. The deployment of hierarchical edge-fog-cloud computing architecture is the potential solution to address the latency issue. In this article, we proposed an interpretable human activity recognition (IHAR) framework that supports HAR, leveraging the advantages of the fog layer. The proposed framework used a deep learning (DL) model at the cloud infrastructure to classify the activities of humans. The trained DL model is made to run on local fog nodes. However, the DL model being black-box does not provide any explanations for the output to make them acceptable by the users and physicians. Hence, this article also incorporated an explainable artificial intelligence model at the fog layer, to gain insights into the classified output of the DL model. The effective model-level insights emphasized the need for explainable HAR. The article also included an analysis of the results of the existing explainable AI models such as LIME and SHAP to understand which model leads to better performance in the domain of HAR. Results show that the SHAP model has a 33% higher success rate in generating explanations when compared to the LIME model.

show abstract

“…The mentioned algorithms can be accomplished in the cloud or on the edge [ 40 , 41 , 42 ]. In view of the requirement that fall detection should be achieved in time and efficiently, edge computing is the best choice, since cloud computing inevitably fails to work once the network connection is unstable.…”

Section: Introductionmentioning

confidence: 99%

A Non-Contact Fall Detection Method for Bathroom Application Based on MEMS Infrared Sensors

Liu

Zhong

et al. 2023

Micromachines

View full text Add to dashboard Cite

The ratio of the elderly to the total population around the world is larger than 10%, and about 30% of the elderly are injured by falls each year. Accidental falls, especially bathroom falls, account for a large proportion. Therefore, fall events detection of the elderly is of great importance. In this article, a non-contact fall detector based on a Micro-electromechanical Systems Pyroelectric Infrared (MEMS PIR) sensor and a thermopile IR array sensor is designed to detect bathroom falls. Besides, image processing algorithms with a low pass filter and double boundary scans are put forward in detail. Then, the statistical features of the area, center, duration and temperature are extracted. Finally, a 3-layer BP neural network is adopted to identify the fall events. Taking into account the key factors of ambient temperature, objective, illumination, fall speed, fall state, fall area and fall scene, 640 tests were performed in total, and 5-fold cross validation is adopted. Experimental results demonstrate that the averages of the precision, recall, detection accuracy and F1-Score are measured to be 94.45%, 90.94%, 92.81% and 92.66%, respectively, which indicates that the novel detection method is feasible. Thereby, this IOT detector can be extensively used for household bathroom fall detection and is low-cost and privacy-security guaranteed.

show abstract

Docker-Based Intelligent Fall Detection Using Edge-Fog Cloud Infrastructure

Cited by 31 publications

References 66 publications

Mutation Detection and Functional Analysis of MSX1, PAX9, AXIN2, and BMP in Nonsyndromic Congenital Missing Teeth Based on Intelligent Image Detection

Mutation Detection and Functional Analysis of MSX1, PAX9, AXIN2, and BMP in Nonsyndromic Congenital Missing Teeth Based on Intelligent Image Detection

IHAR—A fog‐driven interpretable human activity recognition system

A Non-Contact Fall Detection Method for Bathroom Application Based on MEMS Infrared Sensors

Contact Info

Product

Resources

About