Relieved commissioning and human behavior detection in Ambient Assisted Living Systems

Bruckner, Dietmar; Yin, Guo Qing; Faltinger, Armin

doi:10.1007/s00502-012-0015-2

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…Methods for fault tolerance in Ambient Assisted Living were suggested by Ahvar et al [ 50 ]. Data from binary sensors, e.g., movement sensors, may be corrected using a model of the inhabitant behaviour.…”

Section: Literature Survey Resultsmentioning

confidence: 99%

A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living

ElHady

Provost

2018

Sensors

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Ambient Assisted Living (AAL) systems aim to enable the elderly people to stay active and live independently into older age by monitoring their behaviour, provide the needed assistance and detect early signs of health status deterioration. Non-intrusive sensors are preferred by the elderly to be used for the monitoring purposes. However, false positive or negative triggers of those sensors could lead to a misleading interpretation of the status of the elderlies. This paper presents a systematic literature review of the sensor failure detection and fault tolerance in AAL equipped with non-intrusive, event-driven, binary sensors. The existing works are discussed, and the limitations and research gaps are highlighted.

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Section: Literature Survey Resultsmentioning

confidence: 99%

A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living

ElHady

Provost

2018

Sensors

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“…A similar approach to data-based modeling using multi-layer perceptrons, boosting Tree and the Random Forest method was successfully used also in [13] for the optimization of a ventilation system. [14] how to automatically detect the type of sensors that can increase the fault tolerance of different types of sensors. Machine Learning and scenarios detection were examined in [15] and [16], and were also part of the dissertation of Zucker [17]: based on the detection of temporal patterns in the sensor values, Hidden Markov Models were developed.…”

Section: Existing Approaches Todaymentioning

confidence: 99%

Improving energy efficiency of buildings using data mining technologies

Zucker

Malinao

Habib

et al. 2014

2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE)

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Building automation systems record operation data including physical values, system states and operation conditions. This data is stored, but commonly not automatically evaluated. This historic data is the key to efficient operation and to quick recognition of errors and inefficiencies, a potential that is not exploited today. Instead, today the evaluation during operation delivers only alarming in case of system failures. Analysis is commonly done by the facility manager, who uses his experience to interpret data. Methods from data mining and data analysis can contribute to a better understanding of building operation and provide the necessary information to optimize operation, especially in the area of Heating, Ventilation and Air Conditioning (HVAC) systems. Increases in energy efficiency and can be achieved by automated data analysis and by presenting the user energy performance indicators of all relevant HVAC components. The authors take a first step to examine operation data of adsorption chillers using the X-Means algorithm to automate the detection of system states.

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“…The most relevant proposals in our opinion, regarding the number of references to the paper and its relation to crowd and group behavior study, are more deeply explained in this section. Human behavior analysis (HBA) investigations have different applications: improving the quality of life of human beings, in aspects such as support in the health area to detect unusual behaviors, for example falls ofelderly people ir assisted living environments (AAL) (Bruckner et al, 2012), (Banos et al, 2013), (Cardinaux et al, 201 l); surveillance of pedestrians, fights, people running, assaults, ingesting liquor in putrlic places, for example.…”

Section: Classification Of the Level Of Understanding Of Groupsmentioning

confidence: 99%

A Compilation of Methods and Datasets for Group and Crowd Action Recognition

2018

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The human behaviour analysis has been a subject of study in various fields of science (e.g. sociology, psychology, computer science). Specifically, the automated understanding of the behaviour of both individuals and groups remains a very challenging problem from the sensor systems to artificial intelligence techniques. Being aware of the extent of the topic, the objective of this paper is to review the state of the art focusing on machine learning techniques and computer vision as sensor system to the artificial intelligence techniques. Moreover, a lack of review comparing the level of abstraction in terms of activities duration is found in the literature. In this paper, a review of the methods and techniques based on machine learning to classify group behaviour in sequence of images is presented. The review takes into account the different levels of understanding and the number of people in the group.

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Relieved commissioning and human behavior detection in Ambient Assisted Living Systems

Cited by 8 publications

References 11 publications

A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living

A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living

Improving energy efficiency of buildings using data mining technologies

A Compilation of Methods and Datasets for Group and Crowd Action Recognition

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