Typical Behavior Patterns Extraction and Anomaly Detection Algorithm Based on Accumulated Home Sensor Data

Mori, Takahide; Fujii, Asuka; Shimosaka, Masamichi; Noguchi, Hiroshi; Sato, Tomomasa

doi:10.1109/fgcn.2007.226

Cited by 28 publications

(23 citation statements)

References 2 publications

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“…In this case, we cluster activity instances of each activity based on starT ime and duration without considering the day of the week as in [26]. A new activity instance is classified as anomalous or normal based on same technique as Holmes.…”

Section: Clustering Based Approachmentioning

confidence: 99%

“…Clustering based techniques are used in [26] to detect anomalies in timings and durations of different activities. All the above anomaly detection systems often suffer from generating numerous false positives that makes them unreliable.…”

Section: Related Workmentioning

confidence: 99%

“…Otherwise, an anomaly detection system would become unreliable due to excessive false positives and false negatives. Existing anomaly detection systems [13,26,25,24,18,17] either consider each activity instance as an independent data point (point anomalies) or consider sequence of activities together (collective anomalies). However, none of the existing algorithms combines context with point and collective anomalies.…”

Section: Introductionmentioning

confidence: 99%

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Holmes: A Comprehensive Anomaly Detection System for Daily In-home Activities

Hoque

Dickerson

Preum

et al. 2015

2015 International Conference on Distributed Computing in Sensor Systems

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Advances in wireless sensor networks have enabled the monitoring of daily activities of elderly people. The goal of these monitoring applications is to learn normal behavior in terms of daily activities and look for any deviation, i.e., anomalies, so that alerts can be sent to relatives or caregivers. However, human behavior is very complex, and many existing anomaly detection systems are too simplistic which cause many false alarms, resulting in unreliable systems. We present Holmes, a comprehensive anomaly detection system for daily in-home activities. Holmes accurately learns a resident's normal behavior by considering variability in daily activities based not only on a per day basis, but also considering specific days of the week, different time periods such as per week and per month, and collective, temporal, and correlation based features. This approach of learning complicated normal behaviors reduces false alarms. Also, based on resident and expert feedback, Holmes learns semantic rules that explain specific variations of activities in specific scenarios to further reduce false alarms. We evaluate Holmes using data collected from our own deployed system, public data sets, and data collected by a senior safety system provider company from an elderly resident's home. Our evaluation shows that compared to state of the art systems, Holmes reduces false positives and false negatives by at least 46% and 27%, respectively.

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Section: Clustering Based Approachmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Holmes: A Comprehensive Anomaly Detection System for Daily In-home Activities

Hoque

Dickerson

Preum

et al. 2015

2015 International Conference on Distributed Computing in Sensor Systems

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“…In smart homes, it is important to know when the occupants carry out the most activities so that more assistance and support may be allocated to them. In addition, being aware of the most frequent daily activities may also aid in determining any future irregular patterns with a daily routine such as spending long time in the bedroom, relentless roaming around the house, or unusual absence for long periods and so on [3]. Therefore, it is vital to develop a good understanding of the normal behaviour and distinguish any abnormalities and possible trend in the behavioural changes.…”

Section: Introductionmentioning

confidence: 98%

Behavioural Pattern Identification in a Smart Home Using Binary Similarity and Dissimilarity Measures

Mahmoud

Lotfi

Langensiepen

2011

2011 Seventh International Conference on Intelligent Environments

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The aim of this paper is to examine the suitability of binary similarity and dissimilarity measures in identifying frequent and abnormal human behavioural patterns in a smart home. There has been an increasing interest in this subject to help the elderly and disabled people to live alone in their own homes with little help and support from their carer. Similarity and dissimilarity measures have been applied for a wide range of pattern recognition tasks such as character recognition, image retrieval, etc. In this work, the binary similarity and dissimilarity indices are used on data generated from occupancy sensors including door and motion sensors in a smart home. These sensors indicate the presence and absence of the occupant in a specific area in the home. Many measures are introduced in the literature; the focus in this paper is on the measures that give credits to both the positive matches and the mismatching between two sensor values. This paper first gives an overview of the similarity measures to find the most common patterns and then dissimilarity or distance measures are used to identify unexpected or abnormal data. Data from two different case studies are used to validate the accuracy of these measurements.

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“…If iSpace finds that a user is in trouble based on observation, for example, a mobile robot in the space would go to help the user. To realize this, human activity and behaviour recognition methods in smart environments are studied actively (Mori et al, 2007), (Oliver et al, 2004). It is also important to develop actuators including display systems, audio systems and mobile robots in order to provide services based on the observed situations.…”

Section: Introductionmentioning

confidence: 99%