Application on Pervasive Computing in Healthcare – A Review

Sarkar, Paramita; Sinha, Ditipriya

doi:10.17485/ijst/2017/v10i3/110619

Cited by 10 publications

(3 citation statements)

References 35 publications

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“…To analyze the sensor signal pattern of human fall detection, much research works have been carried out. In 2017 (13) has given the review of pervasive computing for fall detection. Methods which detect human falls automatically can be categorized as (1) Thresholding-based approaches (2) Machine Learning approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

Outlier based Human Fall Detection using One-Class Classification

Gupta¹,

Kumar²

2021

IJST

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Objective: This work aims to develop a human fall detection method that is trained using data of routine movement of people only collected from accelerometer sensor to stay away from irregular fall detection model. This work also aims to analyze the effect of calculated features on the fall detection model. Methodology: In the proposed method, The fall detection model is built using one-class classification. At first, data of accelerometer sensor in three directions has been used to detect the fall events. Five feature vectors i.e. resultant, variance, standard deviation, Root Mean square, and Euclidean Norm have been calculated. These four features along with one class SVM have been used to build fall detection model in ways (1) Using only resultant features only (2) Using all calculated features. This model was trained using only activities of daily living (ADL) and tested on both daily living activities and fall activities. Findings: It is found that when the model was built using all calculated features, the sensitivity was 100% and specificity was 94.92% when mobile is in the pocket and; sensitivity was 100% and specificity was 93.61% when mobile is in handbag which is better than when the model was built using resultant features only. We have also studied the effect of individual features on this fall detection model and it is found that variance played a very important role to classify fall activities and ADL activities. Novelty -The proposed fall detection method is built using one-class classification so that the proposed model will be reliable to detect falls in real life. In the proposed work, the effect of features on the fall detection model is also analyzed.

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Section: Literature Reviewmentioning

confidence: 99%

Outlier based Human Fall Detection using One-Class Classification

Gupta¹,

Kumar²

2021

IJST

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“…The pervasive computers acts as a game-changer in the health care industries which throws multiple features to patients such as continues observation of heart rate, blood pressure, glucose level and so on by connecting with health experts all over the world [4,5]. There are a better awareness and engagement of customers with concern to their health results in a boosted growth for remote and personalized health care services.…”

Section: Introductionmentioning

confidence: 99%

Detection of Attacks in Pervasive Computing Using Gated Recurrent Unit Based on Bidirectional Weighted Feature Averaging

Rajasekaran¹,

Magudeeswaran²

2021

Preprint

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In the era of information technology, the new types of cyber-attacks affect the performance of the network, which is very risky and cannot be restored quickly. In pervasive computing, there are more chances for such types of attacks since the personal data of the user is closely connected to the social environment. The research is performed using SNMP-MIB dataset, and feature selection are made by using the Enhanced Salp Swarm Optimization to select the optimal features to identify the attacks by using wrapper techniques. Then, various types of attacks are appropriately distinguished with proposed classifier Gated Recurrent Unit Neural Network based on Bidirectional Weighted Feature Averaging for high detection rate and accuracy. The value of performance metrics obtained from the proposed method outperforms the existing methods in terms of 99.9% accuracy, 99.8% in precision and detection rate is 99% in classifying different types of attacks.

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“…The pervasive computers act as a gamechanger in the health care industries, which throws multiple features to patients such as continuous observation of heart rate, blood pressure, glucose level and so on by connecting with health experts all over the world [3]. There are various types of attacks such as TCP-SYN, UDP flood, ICMP-echo, HTTP flood, Slowloris, Slow post and Brute force attacks is discussed in this paper.…”

Section: Introductionmentioning

confidence: 99%

GRU-BWFA Classifier for Detecting DDoS Attack within SNMP-MIB Dataset

Rajasekar¹,

Magudeeswaran²

2021

Preprint

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With the advancing trends in the field of information technology, the data users were subjected to face differernt of attacks. Hence effective and prompt detection of malicious attacks must be optimized in terms of confidentiality, privacy, availability and integrity. Accordingly this research paper provides an effective mechanism for detecting and classifying DDoS attacks such as TCP-SYN, UDP flood, ICMP echo, HTTP flood, Slowloris Slow Post and Brute Force attack, by utilizing machine learning methods within SNMP-MIB dataset. MIB (Management Information Base) is meant for attack classification database linked to the SNMP (Simple Network Management protocol). Three classifiers are considered such as MLP, Random forest, Adaboost to construct the detection model. Significantly, Gated Recurrent Unit Neural Network based on Bidirectional Weighted Feature Averaging (GRU-BWFA) classifier is utilizing as a proposed classifier for high detection rate and accuracy in distinguishing the mentioned DDoS attacks. Feature selection is performed using the Enhanced Salp Swarm Optimization technique to select the optimal features for identify the attacks. The application of various classifier provides a detailed study on the effectiveness of SNMP-MIB dataset in detecting DDoS attacks. Empirical findings indicate that machine learning methods are highly effective at detecting and classifying the attacks with a higher accuracy rate.

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Application on Pervasive Computing in Healthcare – A Review

Cited by 10 publications

References 35 publications

Outlier based Human Fall Detection using One-Class Classification

Outlier based Human Fall Detection using One-Class Classification

Detection of Attacks in Pervasive Computing Using Gated Recurrent Unit Based on Bidirectional Weighted Feature Averaging

GRU-BWFA Classifier for Detecting DDoS Attack within SNMP-MIB Dataset

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