Activity modeling under uncertainty by trace of objects in smart homes

Amirjavid, Farzad; Bouzouane, Abdenour; Bouchard, Bruno

doi:10.1007/s12652-012-0156-5

Cited by 11 publications

(22 citation statements)

References 10 publications

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“…Computing [12,13]). These contributions were peer-reviewed and well received in the scientific community.…”

Section: Methodsmentioning

confidence: 99%

“…In this sense, a Smart Home can be seen as a challenging big data warehouse in need of an efficient automated computational way to interpret the sensors' data in order to provide high level information about the home's state of normality and needed assistance. More specifically, temporal data requires analysis in order to extract behavioral patterns which can be then used by the Smart Home system for recognizing ongoing activities, predicting subsequent actions, determining a potential situation where assistance is needed and identifying the right moment to provide proper guidance or reminders [12]. This issue is key in developing Smart Home technologies and refers to a very prolific field of artificial intelligence which is called data mining [24] or, more specifically, temporal data mining [25].…”

Section: Smart Homes As a Possible Solutionmentioning

confidence: 99%

“…In our specific context, the Smart Home can be seen as a warehouse storing a variety of data about the resident, coming from different sensors [12]. On one hand, a huge amount of data which includes information about activities is produced, and, on the other hand, we aim to decrease the role of human expertise in knowledge provision in order to achieve more automation in the Smart Home; therefore, data mining (DM) techniques-are extended for making smarter homes [23,[29][30][31].…”

Section: Problem: Data Mining In the Smart Homementioning

confidence: 99%

“…On a theoretical level, the originality of this thesis relies on the proposition of a new unsupervised temporal data-mining model [12] for activity recognition addressing the problem of current temporal approaches based on Allen's framework. This new model incorporates some elements of fuzzy logic [6] in order to take into account the imprecision in the realization of an activity of daily living by a resident.…”

Section: Contributions Of This Thesismentioning

confidence: 99%

“…We obtained very promising results which have been analyzed and compared to existing approaches. Finally, most parts of the contribution presented in this thesis have been published in documents ensuing from reputed international conferences (Springer LNCS proceedings [7], AAAI symposium and workshops [8,9], MAICS [10], IEEE [11]) and a recognized journal (Springer Journal of Ambient Intelligence and Humanized Computing [12,13]). This clearly constitutes recognition showing the potential of the proposed contribution.…”

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confidence: 99%

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fuzzy temporal data-mining model for activity recognition in smart homes /

Amirjavid¹

2013

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At present time, aging of the population is one of the main challenges of the 21st century. The current situation is leading to an increased number of people afflicted with cognitive disorders such as Alzheimer's disease. This group of people suffers from a progressive decline in their abilities to perform what are called the activities of the daily living (ADLs).The consequence of this reality is the urgent need for more home assistance services, as these people desire to continue living independently at home. To address this important issue, Smart Home laboratories such as LIARA, DOMUS and MavHome perform research in order to propose technological solutions for assistance provision to residents of the Smart Home. Assisting people in carrying out their ADLs, increasing quality of life and optimizing spent energy are some of the goals in Smart Home design. Technically speaking, a Smart Home is an ambient environment which, through its embedded sensors, captures data resulting from the observation of activities carried out in this environment. This data is then analyzed by artificial intelligence techniques in order to provide information about home state normality and needed assistance. In the end, the system aims to intervene by providing guidance through its actuators. In this context, activity recognition becomes a key element in order to be able to provide adequate information services at the right moment.This thesis aims to contribute to this important challenge relating to activity recognition in the Smart Home designed for cognitive assistance. This contribution follows in the footsteps of temporal data mining and activity recognition approaches, and proposes a new way to automatically recognize and memorize ADLs from low-level sensors. From a formal point of view, the originality of the thesis relies on the proposition of a new unsupervised temporal datamining model for activity recognition addressing the problem of current temporal approaches based on Allen's framework. This new model incorporates some applications of fuzzy logic in order to take into account the uncertainty present in the realization of daily living activities by the resident. More specifically, we propose an extension of the fuzzy clustering technique in order to cluster the observations based on the degree of similarity between observations, so that activities are modeled and recognized. Moreover, anomaly recognition, decision making for assistance provision and judgment for simultaneous activities are some of the applicative contributions of this thesis. From a practical and experimental standpoint, the contribution of this research has been validated in order to evaluate how it would perform in a realistic context. To achieve this, we used MATLAB software as a simulation platform to test the proposed model. We then performed a series of tests which took the form of several case studies relating to common activities of daily living, in order to show the functionality and efficiency of the proposed temporal data-mining approa...

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“…Computing [12,13]). These contributions were peer-reviewed and well received in the scientific community.…”

Section: Methodsmentioning

confidence: 99%