IoT-based smart healthcare video surveillance system using edge computing

Rajavel, Rajkumar; Ravichandran, Sathish Kumar; Harimoorthy, Karthikeyan; Nagappan, Partheeban; Gobichettipalayam, Kanagachidambaresan Ramasubramanian

doi:10.1007/s12652-021-03157-1

Cited by 85 publications

(31 citation statements)

References 28 publications

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“…In the future, this research study can be enhanced with an edge or a for computing-based peer-to-peer platform for improving the performance of the prediction system. 30 This could help to makes the disease prediction in the nearby edge computing resource rather than doing prediction in the remote cloud computing resources. In addition, a negotiation framework from the recent studies 3132 can be exploited in the healthcare system to negotiate the best healthcare services for real-time diagnosis of diabetic retinopathy.…”

Section: Resultsmentioning

confidence: 99%

Cloud‐based onboard prediction and diagnosis of diabetic retinopathy

Sundharamurthy¹,

Kaliappan

2021

Concurrency and Computation

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Diabetic retinopathy remains the most daunting microvascular complication of diabetic disease. Major risk factors of diabetic retinopathy include long-term diabetics, improper blood glucose level, and dyslipidemia. In order to investigate the presence of diabetic retinopathy-related features and to deal with the diagnosis of various diabetic severity stages, there is a need for a cloud-based automated screening system. So, the development of novel biomarkers should take the advantage of objective measurement and evaluation indicating the biological and pathogenic processes to all the therapeutic interventions. Current state-of-the-art research studies lack accommodating large sample size, cross-validation among the diverse populations, time, and cost-effective diagnosis techniques. To overcome these challenges, a cloud-based diabetic retinopathy prediction system is proposed using the recurrent convolution neural network classifier model. The proposed prediction system can provide risk stratification, optimized resource allocation, a severe form of disease prediction, risk control, and preventive interventions during the screening and diagnosis process. It also provides prognostic information to patients stating various cost-effective predictive responses according to treatment modalities. Moreover, the proposed system clinical decision-making will outperform the existing classifiers in terms of prediction accuracy, sensitivity, specificity, precision, recall, and F1-score.

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Section: Resultsmentioning

confidence: 99%

Cloud‐based onboard prediction and diagnosis of diabetic retinopathy

Sundharamurthy¹,

Kaliappan

2021

Concurrency and Computation

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“…To address these problems, this article proposes an artificial intelligence (AI)-based fall detection method that operates on edge computing architecture like [56], namely, pose estimation-based fall detection methodology (PEFDM), which is based on recognizing human body postures. The proposed PEFDM runs smoothly on mainstream edge computing systems that possess AI computing capabilities.…”

Section: ) Cost Problemmentioning

confidence: 99%

A Pose Estimation-Based Fall Detection Methodology Using Artificial Intelligence Edge Computing

Chang

Hsu

Chen³

2021

IEEE Access

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As the population worldwide continues to age and the percentage of elderly people continues to increase, falls have been become the second leading cause of death from accidental or unintentional injuries. Although many imaging sensing devices have been used to detect falls for elderly people, most involve using the Internet to transfer images taken by a camera to a large back-end server, which performs the necessary calculations; however, algorithm limitations and computational complexity may cause bottlenecks in image outflow, and the image transfer can result in privacy problems. To address these problems, in this paper, an artificial intelligence (AI) fall detection method is proposed that operates using an edge computing architecture, called the pose estimation-based fall detection methodology (PEFDM), which is based on a human body posture recognition technique. The proposed PEFDM can effectively reduce the computational load, runs smoothly on mainstream edge computing systems and possesses artificial intelligence computing capabilities. By using edge computing, the privacy and upload bandwidth issues caused by image outflow can be eliminated. Experiments with real humans show that the PEFDM can detect falls for elderly people with a recognition accuracy of up to 98.1%.

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“…This limitation due to overwhelming request processing and high bandwidth utilization is required during remote monitoring of patients. It can be resolved through the enforcement of an edge‐cloud integrated platform that can migrate the prediction and rehabilitation monitoring services from the cloud to nearby edge computing nodes 52 . As a result, the healthcare system can improve the onboard prediction and diagnosis with quick response time and less bandwidth utilization factors.…”

Section: Experimental Evaluationsmentioning

confidence: 99%

Cloud‐assisted Parkinson disease identification system for remote patient monitoring and diagnosis in the smart healthcare applications

Harimoorthy

Thangavelu²

2021

Concurrency and Computation

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Summary The development of a cloud‐based Parkinson's disease identification system is identified as one of the most challenging issues for predictive telediagnosis and telemonitoring in the emerging smart healthcare applications. The traditional way of Parkinson's disease identification by clinical parameters is more costly and uncomfortable for rural people to avail the testing and diagnosis from the remote place. So, the cloud‐assisted Parkinson's disease identification system (CAPDIS) is proposed based on non‐clinical parameters with patient‐centric and cost‐effective features for helping the poor patients living in rural as well as urban areas. In addition, the proposed system diagnoses the remote patient by examining the symptoms like dysphonia which is identified as the most severe neurodegenerative disorder in the world. Further, the proposed system has experimented with the benchmark voice dataset collected from the University of California‐Irvine (UCI) repository. It shows that the proposed CAPDIS system with adaptive linear kernel support vector machines (k‐SVM) classifier has significant improvements on detection accuracy, specificity, sensitivity, and Matthews's correlation coefficient scores while comparing to the existing classifiers. Therefore, the proposed adaptive linear k‐SVM classifiers provide 10% improvements on prediction accuracy and F1‐Score over the existing polynomial, radial basis, and sigmoidal‐based k‐SVM classifiers.

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IoT-based smart healthcare video surveillance system using edge computing

Cited by 85 publications

References 28 publications

Cloud‐based onboard prediction and diagnosis of diabetic retinopathy

Cloud‐based onboard prediction and diagnosis of diabetic retinopathy

A Pose Estimation-Based Fall Detection Methodology Using Artificial Intelligence Edge Computing

Cloud‐assisted Parkinson disease identification system for remote patient monitoring and diagnosis in the smart healthcare applications

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