Temporal pattern discovery for anomaly detection in a smart home

Jakkula, Vikramaditya; Cook, Diane J.; Crandall, Aaron S.

doi:10.1049/cp:20070390

Cited by 48 publications

(42 citation statements)

References 7 publications

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“…However, most of it is based on cameras [6,7], wearable sensors [8,9] and RFID sensors [10,11], and studies on AAR in AmI environments using non-obtrusive and pervasive sensors are rare. In 2007 and 2008, Jakkula et al [12,13] proposed recognizing abnormal activities using non-obtrusive sensors. In their research, they used temporal logic (before, after, meets, overlaps, starts ...) to identify temporal relationships between events and detected anomalies by calculating the probability of a given event occurring or not occurring.…”

Section: Open Accessmentioning

confidence: 99%

Hidden State Conditional Random Field for Abnormal Activity Recognition in Smart Homes

Chen

Gao

2015

Entropy

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Abstract:As the number of elderly people has increased worldwide, there has been a surge of research into assistive technologies to provide them with better care by recognizing their normal and abnormal activities. However, existing abnormal activity recognition (AAR) algorithms rarely consider sub-activity relations when recognizing abnormal activities. This paper presents an application of the Hidden State Conditional Random Field (HCRF) method to detect and assess abnormal activities that often occur in elderly persons' homes. Based on HCRF, this paper designs two AAR algorithms, and validates them by comparing them with a feature vector distance based algorithm in two experiments. The results demonstrate that the proposed algorithms favorably outperform the competitor, especially when abnormal activities have same sensor type and sensor number as normal activities.

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Section: Open Accessmentioning

confidence: 99%

Hidden State Conditional Random Field for Abnormal Activity Recognition in Smart Homes

Chen

Gao

2015

Entropy

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“…Notable examples of this approach employ Hidden Markov Models (HMMs) in conjunction with learning techniques for inferring transition probabilities [6,7]. Model-driven approaches follow a complementary strategy in which patterns of observations are modeled from first principles rather than learned or inferred from large quantities of data [8,9]. Data-and model-driven approaches have complementary strengths: the former provide an effective way to recognize elementary activities from large amounts of continuous data; conversely, model-driven approaches are useful when the criteria for recognizing human activities are given by crisp rules that are clearly identifiable.…”

Section: B Context Recognitionmentioning

confidence: 99%

GiraffPlus: Combining social interaction and long term monitoring for promoting independent living

Coradeschi

Cesta

Cortellessa

et al. 2013

2013 6th International Conference on Human System Interactions (HSI)

112

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Abstract. Early detection and adaptive support to changing individual needs related to ageing is an important challenge in today's society. In this paper we present a system called GiraffPlus that aims at addressing such a challenge and is developed in an on-going European project. The system consists of a network of home sensors that can be automatically configured to collect data for a range of monitoring services; a semi-autonomous telepresence robot; a sophisticated context recognition system that can give high-level and long term interpretations of the collected data and respond to certain events; and personalized services delivered through adaptive user interfaces for primary users. The system performs a range of services including data collection and analysis of long term trends in behaviors and physiological parameters (e.g. relating to sleep or daily activity); warnings, alarms and reminders; and social interaction through the telepresence robot. The latter is based on the Giraff telepresence robot, which is already in place in a number of homes. Particular emphasis is put on user evaluation outside the laboratories. A distinctive aspect of the project is that the GiraffPlus system will be installed and evaluated in at least 15 homes of elderly people. The concept of "useworthiness" is central in order to assure that the GiraffPlus system provides services that are easy to use and worth using. In addition, by using existing and affordable components we strive to achieve a system that is affordable and close to commercialization.

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“…One of the main challenges related to this Smart Home concept concerns the huge amount of data resulting from the observation of the ongoing activities through smart sensors [23]. 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.…”

Section: Smart Homes As a Possible Solutionmentioning

confidence: 99%

“…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]. The complexity of daily activity recognition in a Smart…”

Section: Problem: Data Mining In the Smart Homementioning

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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Temporal pattern discovery for anomaly detection in a smart home

Cited by 48 publications

References 7 publications

Hidden State Conditional Random Field for Abnormal Activity Recognition in Smart Homes

Hidden State Conditional Random Field for Abnormal Activity Recognition in Smart Homes

GiraffPlus: Combining social interaction and long term monitoring for promoting independent living

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

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