Abnormal behaviours identification for an elder's life activities using dissimilarity measurements

Mahmoud, Sawsan M.; Lotfi, Ahmad; Langensiepen, Caroline

doi:10.1145/2141622.2141653

Cited by 22 publications

(8 citation statements)

References 12 publications

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“…In the statistical methods, the dissimilarity-based model is developed [17], where an index is used to measure the degree of resemblance between the normal and abnormal behavior. They performed the experiments on the two different datasets with single inhabitants at home.…”

Section: ) Statistical Methodsmentioning

confidence: 99%

Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

2019

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Anomaly detection has attracted considerable attention from the research community in the past few years due to the advancement of sensor monitoring technologies, low-cost solutions, and high impact in diverse application domains. Sensors generate a huge amount of data while monitoring the physical spaces and objects. These huge collected data streams can be analyzed to identify unhealthy behaviors. It may reduce functional risks, avoid unseen problems, and prevent downtime of the systems. Many research methodologies have been designed and developed to determine such anomalous behaviors in security and risk analysis domains. In this paper, we present the results of a systematic literature review about anomaly detection techniques except for these dominant research areas. We focus on the studies published from 2000 to 2018 in the application areas of intelligent inhabitant environments, transportation systems, health care systems, smart objects, and industrial systems. We have identified a number of research gaps related to the data collection, the analysis of imbalanced large datasets, limitations of statistical methods to process the huge sensory data, and few research articles in abnormal behavior prediction in real scenarios. Based on our analysis, researchers and practitioners can acquaint themselves with the existing approaches, use them to solve real problems, and/or further contribute to developing novel techniques for anomaly detection, prediction, and analysis.INDEX TERMS Statistical learning, machine learning, intelligent environments, smart objects, intelligent transportation systems, industrial systems.

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Section: ) Statistical Methodsmentioning

confidence: 99%

Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

2019

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“…The similarity (or distance) measure quantifies the similarity between two objects, which plays an important role in pattern recognition, clustering, classification, and recommendation problems (Guo et al 2013, Mahmoud et al 2011. In particular, binary similarity and distance measure is one of the most commonly used similarity measures.…”

Section: Similarity Measure Of Collapse Distributionmentioning

confidence: 99%

Improving the Accuracy of near Real-Time Seismic Loss Estimation using Post-Earthquake Remote Sensing Images

Zeng

et al. 2018

Earthquake Spectra

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With the rapid development of remote sensing technology, satellite or aerial images from the disaster area become available within 24 hours after an earthquake. The collapsed buildings can be easily identified from these images. In this work, a framework for near real-time seismic loss estimation for regional buildings is proposed, which improves the accuracy of nonlinear time-history analysis (THA)-based loss estimations by taking advantage of the identified building collapse scene of the disaster area. Specifically, a series of THA are performed for the target regional buildings, thereby generating a number of simulation results. Those simulation results that bear strong similarities to the identified collapse scene are identified as the optimal solutions, which will be used to estimate the seismic loss. The simulation results of the case studies signify that the use of the identified building collapse scene leads to much closer estimations to actual economic losses.

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“…One of the important roles a smart home can play is to automatically find anomalies. Anomalies can be viewed as a threat to an individual’s safety in the home or even as a threat to their health [ 61 , 62 , 63 , 64 , 65 ].…”

Section: Anomaly Detectionmentioning

confidence: 99%

Activity Learning as a Foundation for Security Monitoring in Smart Homes

Dahmen

Thomas

Cook

et al. 2017

Sensors

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Smart environment technology has matured to the point where it is regularly used in everyday homes as well as research labs. With this maturation of the technology, we can consider using smart homes as a practical mechanism for improving home security. In this paper, we introduce an activity-aware approach to security monitoring and threat detection in smart homes. We describe our approach using the CASAS smart home framework and activity learning algorithms. By monitoring for activity-based anomalies we can detect possible threats and take appropriate action. We evaluate our proposed method using data collected in CASAS smart homes and demonstrate the partnership between activity-aware smart homes and biometric devices in the context of the CASAS on-campus smart apartment testbed.

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Abnormal behaviours identification for an elder's life activities using dissimilarity measurements

Cited by 22 publications

References 12 publications

Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

Improving the Accuracy of near Real-Time Seismic Loss Estimation using Post-Earthquake Remote Sensing Images

Activity Learning as a Foundation for Security Monitoring in Smart Homes

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