Model-Based Approach for Anomaly Detection in Smart Home Inhabitant Daily Life

Fouquet, Kevin; Faraut, Grégory; Lesage, Jean-Jacques

doi:10.23919/acc50511.2021.9483053

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…To evaluate the consistency between the observed behavior and the expected one described by the model ( 4), the approach presented in [10] is used. Each time a new occurrence of an activity is detected, the difference between its duration and the expected median time is computed:…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Activity duration evaluation [10] Long term deviation detection using data forecasting behavioral anomalies which may be caused by specific health trouble. For instance, authors in [3], [10] model activity duration by probabilistic distribution to detect anomalies which might be due to a loss of appetite or cognitive decline for instance. In [5], the authors observe the duration of room transition, allowing to evaluate loss of mobility which can be due to frailty.…”

Section: + -mentioning

confidence: 99%

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Long-term deviation detection in human behavior

Fouquet

Faraut

Lesage

2021

2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)

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World population ageing causes an important increase of people needing specific health care and monitoring. Dedicated institutions exist, but most of the elderly prefer to keep their autonomy for economic and personal reasons. To ensure a good quality of life and health to this population, Health at Home (HaH) solutions are explored. Many works focus on monitoring smart home inhabitant behavior to detect changes which might be due to health problems. These approaches are efficient to detect accident or short-term diseases such as a cold or influenza but tend to detect too tardily diseases which provoke slow declines in behavior. This is a problem as the elderly are likely to suffer from such troubles and early detection allows for better diagnosis and may help to prevent or reduce future worsening. In this paper, a novel approach for the detection of long-term behavior changes is introduced. It focuses on activity duration as this indicator is influenced by most diseases and give clear information about the inhabitant health status. This paper proposes data forecasting to detect future anomalies to assess existence of evolution in the current behavior. Information is sent to medical staff to refine their prognostic and adapt their treatment or call for a medical appointment. A case study based on a real smart home simulating a worst-case scenario attests for the efficiency of the approach and its resilience.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: + -mentioning

confidence: 99%

Long-term deviation detection in human behavior

Fouquet

Faraut

Lesage

2021

2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)

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“…Elderly suffering from dementia [3], [24], [29], [36], [45], [51] Rule-based/ threshold indices [19], [31]- [33], [38], [41], [70] Loss of appetite and urinary tract infection [37] Elderly suffering from Alzheimer [62]…”

Section: Nonaccidentalmentioning

confidence: 99%

“…Apart from common illnesses in elders like dementia, the ABD system in ADL can identify many more health-related issues such as loss of appetite and even urinary tract infection [37]. This could be implemented as the monitoring of ADL can produce vast information about the users' health conditions.…”

Section: • Health Monitoring Systemmentioning

confidence: 99%

A Review of Abnormal Behavior Detection in Activities of Daily Living

et al. 2023

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Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend. INDEX TERMS abnormal behavior detection, activities of daily living

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