Forecasting the behavior of an elderly using wireless sensors data in a smart home

Suryadevara, Nagender Kumar; Mukhopadhyay, Subhas Chandra; Wang, R.; Rayudu, Ramesh

doi:10.1016/j.engappai.2013.08.004

Cited by 228 publications

(110 citation statements)

References 25 publications

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“…The work in [39] shows CASE, a framework that recognizes human activities by discovering the frequent episodes from occupant-related data based on the a priori algorithm [40]. The work in [41] shows a probabilistic-based approach to forecast smart home occupants' behavior and wellness by monitoring the daily usage of home appliances. For example, elderly people's behavior follows some regularity in their activity execution such as eating at 7 a.m.…”

Section: Related Workmentioning

confidence: 99%

Mining Productive-associated Periodic Frequent Pattern in Body Sensor Data for Smart Home Care

Ismail¹,

Hassan²

2017

Preprint

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Abstract:The understanding of various health-oriented vital sign data generated from body sensor networks (BSNs) and discovery of the associations between the generated parameters is an important task that may assist and promote important decision making in healthcare. For example, in a smart home scenario where occupants' health status is continuously monitored remotely, it is essential to provide the required assistance when an unusual or critical situation is detected in their vital sign data. In this paper, we present an efficient approach to mine the periodic patterns obtained from BSN data. In addition, we employ a correlation test on the generated patterns and introduce productiveassociated periodic-frequent patterns as the set of correlated periodic-frequent items. The combination of these measures has the advantage of empowering healthcare providers and patients to raise the quality of diagnosis as well as improve treatment and smart care, especially for elderly people in a smart home. We develop an efficient algorithm named PPFP-growth (Productive Periodic-Frequent Pattern-growth) to discover all productive-associated periodic frequent patterns using these measures. PPFP-growth is efficient and the productiveness measure removes uncorrelated periodic items. An experimental evaluation on synthetic and real datasets shows the efficiency of the proposed PPFP-growth algorithm, which can filter a huge number of periodic patterns to reveal only the correlated ones.Keywords: Body sensor network; Smart home, knowledge discovery in BSN data; frequent patterns; periodic patterns and productive pattern.

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Section: Related Workmentioning

confidence: 99%

Mining Productive-associated Periodic Frequent Pattern in Body Sensor Data for Smart Home Care

Ismail¹,

Hassan²

2017

Preprint

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“…For two special cases with 2 out of N =3 and 4 possible transmit antennas, [19] derived the accurate analytical expressions of bit-error rate (BER) of Alamouti scheme with TAS, and [20] derived Chernoff bounds for same system and used these bounds to quantify the diversity gain of the system using antenna selection. For a special case with 2 out of N =3 possible transmit antennas, [21] presented the performance analysis for an antenna selection system considering both erroneous CSI and transmit power allocation. Same assumptions as in [19]- [21], [22] applies the results of asymptotic performance of wireless communications with generalized selection combining over random fading channels at high signal-to-noise ratio (SNR) in [23], the asymptotic performance of the Alamouti scheme with transmit antenna selection has been investigated taking into account imperfect antenna subset selection.…”

Section: International Journal On Smart Sensing and Intelligent Systementioning

confidence: 99%

Performance Bound Analysis on Transmit Antennas Selection Systems Considering Erroneous Channel Information

Zhuang

2015

International Journal on Smart Sensing and Intelligent Systems

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“…A first system for Ambient Intelligence Prediction in a smart home is proposed by Suryadevara et al [21,22]. The method consists of two equations that evaluate the level of abnormality in an activity of daily living, observing the usage of a given appliance.…”

Section: Indicators For Behavioral Drift Detectionmentioning

confidence: 99%

Behavior Drift Detection Based on Anomalies Identification in Home Living Quantitative Indicators

et al. 2018

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Home Automation and Smart Homes diffusion are providing an interesting opportunity to implement elderly monitoring. This is a new valid technological support to allow in-place aging of seniors by means of a detection system to notify potential anomalies. Monitoring has been implemented by means of Complex Event Processing on live streams of home automation data: this allows the analysis of the behavior of the house inhabitant through quantitative indicators. Different kinds of quantitative indicators for monitoring and behavior drift detection have been identified and implemented using the Esper complex event processing engine. The chosen solution permits us not only to exploit the queries when run "online", but enables also "offline" (re-)execution for testing and a posteriori analysis. Indicators were developed on both real world data and on realistic simulations. Tests were made on a dataset of 180 days: the obtained results prove that it is possible to evidence behavior changes for an evaluation of a person's condition.

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Forecasting the behavior of an elderly using wireless sensors data in a smart home

Cited by 228 publications

References 25 publications

Mining Productive-associated Periodic Frequent Pattern in Body Sensor Data for Smart Home Care

Mining Productive-associated Periodic Frequent Pattern in Body Sensor Data for Smart Home Care

Performance Bound Analysis on Transmit Antennas Selection Systems Considering Erroneous Channel Information

Behavior Drift Detection Based on Anomalies Identification in Home Living Quantitative Indicators

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