A Survey on Anomalous Behavior Detection for Elderly Care Using Dense-Sensing Networks

Deep, Samundra; Zheng, Xi; Karmakar, Chandan; Yu, Dongjin; Hamey, Len; Jin, Jiong

doi:10.1109/comst.2019.2948204

Cited by 71 publications

(44 citation statements)

References 119 publications

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“…However, some alerts can be false positives, and to address such errors, dense sensor networks along with medical devices can help to reduce false positives. Furthermore, classification techniques such as Hidden Markov Model (HMM) [31] can be used to classify anomalies in PGHD. This model can be deployed on cloud or gateway to detect false alerts.…”

Section: Future Smart Connected Ecosystem and Technological Solutionsmentioning

confidence: 99%

Future Smart Connected Communities to Fight COVID-19 Outbreak

et al. 2021

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Internet of Things (IoT) has grown rapidly in the last decade and continue to develop in terms of dimension and complexity, offering wide range of devices to support diverse set of applications. With ubiquitous Internet, connected sensors and actuators, networking and communication technology along with artificial intelligence (AI), smart cyber-physical systems (CPS) provide services rendering assistance and convenience to humans in their daily lives. However, the recent outbreak of COVID-19 (also known as coronavirus) pandemic has exposed and highlighted the limitations of contemporary technological deployments especially to contain the wide spread of this disease. IoT and smart connected technologies together with data-driven applications can play a crucial role not only in the prevention, mitigation or continuous remote monitoring of patients, but also enable prompt enforcement of guidelines, rules and administrative orders to contain such future outbreaks. In this paper, we envision an IoT and data supported connected ecosystem designed for intelligent monitoring, pro-active prevention and control, and mitigation of COVID-19 and similar epidemics. We propose a gamut of synergistic applications and technology systems for various smart infrastructures including E-Health, smart home, supply chain management, transportation, and city, which will work in convergence to develop ‘pandemic-proof’ future smart communities. We also present a generalized cloud-enabled IoT implementation framework along with scientific solutions, which can be adapted and extended to deploy smart connected ecosystem scenarios using widely used Amazon Web Services (AWS) cloud infrastructures. In addition, we also implement an E-Health RPM use case scenario to demonstrate the need and practicality for smart connected communities. Finally, we highlight challenges and research directions which need thoughtful consideration and across the board cooperation among stakeholders to build resilient communities against future pandemics.

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Section: Future Smart Connected Ecosystem and Technological Solutionsmentioning

confidence: 99%

Future Smart Connected Communities to Fight COVID-19 Outbreak

et al. 2021

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“…Such systems implicitly rely on the recognition and the representation of human activities. These approaches are grouped, as in reference [ 39 , 42 , 43 ], into three different classes, namely point anomaly, collective anomaly, and contextual anomaly…”

Section: Related Workmentioning

confidence: 99%

“…The basic way to identify anomalies is to look at the data points that do not conform to the properties of the regular pattern. Two different strategies have, therefore, been introduced in the literature [ 10 , 43 ]: discriminating and profiling which is based on behavior modeling [ 53 , 54 , 55 ] to build a normal behavior.…”

Section: Related Workmentioning

confidence: 99%

A Framework for Detecting and Analyzing Behavior Changes of Elderly People over Time Using Learning Techniques

Zekri

Delot

Thilliez

et al. 2020

Sensors

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A sensor-rich environment can be exploited for elder healthcare applications. In this work, our objective was to conduct a continuous and long-term analysis of elderly’s behavior for detecting changes. We indeed did not study snapshots of the behavior but, rather, analyzed the overall behavior evolution over long periods of time in order to detect anomalies. Therefore, we proposed a learning method and formalize a normal behavior pattern for elderly people related to her/his Activities of Daily Living (ADL). We also defined a temporal similarity score between activities that allows detecting behavior changes over time. During the periods of time when behavior changes occurred, we then focused on each activity to identify anomalies. Finally, when a behavior change occurred, it was also necessary to help caregivers and/or family members understand the possible pathology detected in order for them to react accordingly. Therefore, the framework presented in this article includes a fuzzy logic-based decision support system that provides information about the suspected disease and its severity.

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“…To date, and due to the increasing power processing capabilities of the different mobile devices, the Adaboost method is one of the most used methods, and it reports reliable results [24][25][26][27][28][29][30][31][32]. The motivation of this systematic review is to evaluate the reliability of the Adaboost method for daily activities and environment recognition using the sensors available in mobile devices for further implementation of a framework [33][34][35][36][37][38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Identification of Daily Activites and Environments Based on the AdaBoost Method Using Mobile Device Data: A Systematic Review

et al. 2020

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Using the AdaBoost method may increase the accuracy and reliability of a framework for daily activities and environment recognition. Mobile devices have several types of sensors, including motion, magnetic, and location sensors, that allow accurate identification of daily activities and environment. This paper focuses on the review of the studies that use the AdaBoost method with the sensors available in mobile devices. This research identified the research works written in English about the recognition of daily activities and environment recognition using the AdaBoost method with the data obtained from the sensors available in mobile devices that were published between 2012 and 2018. Thus, 13 studies were selected and analysed from 151 identified records in the searched databases. The results proved the reliability of the method for daily activities and environment recognition, highlighting the use of several features, including the mean, standard deviation, pitch, roll, azimuth, and median absolute deviation of the signal of motion sensors, and the mean of the signal of magnetic sensors. When reported, the analysed studies presented an accuracy higher than 80% in recognition of daily activities and environments with the Adaboost method.

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A Survey on Anomalous Behavior Detection for Elderly Care Using Dense-Sensing Networks

Cited by 71 publications

References 119 publications

Future Smart Connected Communities to Fight COVID-19 Outbreak

Future Smart Connected Communities to Fight COVID-19 Outbreak

A Framework for Detecting and Analyzing Behavior Changes of Elderly People over Time Using Learning Techniques

Identification of Daily Activites and Environments Based on the AdaBoost Method Using Mobile Device Data: A Systematic Review

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