Explicit State Duration HMM for Abnormality Detection in Sequences of Human Activity

Lühr, Sebastian; Venkatesh, Svetha; West, Geoff; Bui, Hung

doi:10.1007/978-3-540-28633-2_125

Cited by 26 publications

(30 citation statements)

References 1 publication

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“…In addition, they have an easy naming convention, making it easier to recognize, interpret and use the temporal relations that are identified. There are projects which employ sequential information to predict activities [4], and other methods for identifying suspicious states in a smart environment have been researched [9].…”

Section: Introductionmentioning

confidence: 99%

Temporal pattern discovery for anomaly detection in a smart home

Jakkula

Cook

Crandall

2007

3rd IET International Conference on Intelligent Environments (IE 07)

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The temporal nature of data collected in a smart environment provides us with a better understanding of patterns over time. Detecting anomalies in such datasets is a complex and challenging task. To solve this problem, we suggest a solution using temporal relations. Temporal pattern discovery based on modified Allen's temporal relations [5] has helped discover interesting patterns and relations on smart home datasets [10]. This paper describes a method of discovering temporal relations in smart home datasets and applying them to perform anomaly detection process on the frequently-occurring events. We also include experimental results, performed on real and synthetic datasets.

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Section: Introductionmentioning

confidence: 99%

Temporal pattern discovery for anomaly detection in a smart home

Jakkula

Cook

Crandall

2007

3rd IET International Conference on Intelligent Environments (IE 07)

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“…These include [9,17]. Third, one is about abnormal human behavior detection, which recognizes human Activities of Daily Living (ADL) [7,12].…”

Section: Related Workmentioning

confidence: 99%

“…We first calculate best threshold values using both method 1 (M1, equation (12)) and method 2 (M2, equation (13)). 50% of T1 data set is used for as a gold standard of normal cases.…”

Section: Fixed Thresholdmentioning

confidence: 99%

Abnormal human behavioral pattern detection in assisted living environments

Park

Lin

Metsis

et al. 2010

Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments

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In recent years, there is a growing interest about assisted living environments especially for the elderly who live alone, due to the increasing number of aged people. In order for them to live safe and healthy, we need to detect abnormal behavior that may cause severe and emergent situations for the elderly. In this work, we suggest a method that detects abnormal behavior using wireless sensor networks. We model an episode that is a series of events, which includes spatial and temporal information about the subject being monitored. We define a similarity scoring function that compares two episodes taking into consideration temporal aspects. We propose a way to determine a threshold to divide episodes into two groups that reduces wrong classification. Weights on individual functions that consist the similarity function are determined experimentally so that they can produce the good results in terms of area under curve in receiver operating characteristic (ROC) curve.

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“…For the activity duration, Luhr et al [30] use the explicit state duration HMM (ESD-HMM), in which a duration variable is introduced in a standard HMM. Duong et al [1] introduce the switching hidden semi-Markov model (S-HSMM), which implicitly exploits the benefit of both the inherent hierarchical organization of the activities and their typical duration.…”

Section: Daily Life Activity Monitoringmentioning

confidence: 99%

“…Furthermore, in a healthcare system, the activity recognition can help the rehabilitation of patients, such as the automatic recognition of patient's action to facilitate the rehabilitation processes. There have been numerous research efforts reported for various applications based on human activity recognition, more specifically, home abnormal activity [1], ballet activity [2], tennis activity [3,4], soccer activity [5], human gestures [6], sport activity [7,8], human interaction [9], pedestrian traffic [10] and simple actions [11][12][13][14][15][16][17][18][19][20][21][22], and healthcare applications [1,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38]. In this paper, the video based technologies for human activity recognition will be extensively reviewed and discussed.…”

Section: Introductionmentioning

confidence: 99%

A Review on Video-Based Human Activity Recognition

et al. 2013

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This review article surveys extensively the current progresses made toward video-based human activity recognition. Three aspects for human activity recognition are addressed including core technology, human activity recognition systems, and applications from low-level to high-level representation. In the core technology, three critical processing stages are thoroughly discussed mainly: human object segmentation, feature extraction and representation, activity detection and classification algorithms. In the human activity recognition systems, three main types are mentioned, including single person activity recognition, multiple people interaction and crowd behavior, and abnormal activity recognition. Finally the domains of applications are discussed in detail, specifically, on surveillance environments, entertainment environments and healthcare systems. Our survey, which aims to provide a comprehensive state-of-the-art review of the field, also addresses several challenges associated with these systems and applications. Moreover, in this survey, various applications are discussed in great detail, specifically, a survey on the applications in healthcare monitoring systems. OPEN ACCESSComputers 2013, 2 89

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Explicit State Duration HMM for Abnormality Detection in Sequences of Human Activity

Cited by 26 publications

References 1 publication

Temporal pattern discovery for anomaly detection in a smart home

Temporal pattern discovery for anomaly detection in a smart home

Abnormal human behavioral pattern detection in assisted living environments

A Review on Video-Based Human Activity Recognition

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