Detecting and exploring deviating behaviour of smart home residents

The number of people with dementia is rising every year, and it is getting more expensive to provide care for them in specially designed care centres. Furthermore, some people with dementia prefer to live independently, and to be accompanied by informal and family caregivers. One way to meet this requirement of people with dementia is to place them in smart homes. Smart homes facilitate extra care for people with dementia with automated and semi-automated support. However, smart homes need to be designed in a way that addresses the specific needs of people with dementia. One suggested approach is the usercentred design method, which involves the participation of people with dementia and their caregivers during the design process. This paper presents a literature review of smart homes for people with dementia and user-centred design. The paper attempts to provide a sound review, which can be useful for designers and developers of smart homes for people with dementia.smart homes, people with dementia, user-centred design, literature review

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“…The services provided by smart homes to people with dementia can be classified into two groups (Lundström et al 2016):…”

Section: Smart Home Services For People With Dementiamentioning

confidence: 99%

A Literature Review on the Design of Smart Homes for People with Dementia Using a User-Centred Design Approach

Raei

Bouchachia

2016

Electronic Workshops in Computing

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“…In distance-based methods (Aztiria, Farhadi & Aghajan, 2013;Lundström, Järpe & Verikas, 2016;Park, et al, 2010;Yuan & Herbert, 2014), anomalies are observations that are very different from learned behaviors based on a distance or similarity measure. They also recognize that considering periodic variations (routines for weekdays and weekends) can improve the precision of anomaly detection.…”

Section: Behavior Models For Anomaly Detectionmentioning

confidence: 99%

“…They also recognize that considering periodic variations (routines for weekdays and weekends) can improve the precision of anomaly detection. In distance-based methods (Aztiria, Farhadi & Aghajan, 2013;Lundström, Järpe & Verikas, 2016;Park, et al, 2010;Yuan & Herbert, 2014), anomalies are observations that are very different from learned behaviors based on a distance or similarity measure.…”

Section: Behavior Models For Anomaly Detectionmentioning

confidence: 99%

“…However, their similarity measure (Aztiria et al, 2013) only considers the sequential elements and not the similarity between context conditions. Lundström et al (2016) consider interpretations (by clustering sensor events) but do not consider sequential information. They consider temporal and spatial characteristics but use value-based similarity to detect anomalies.…”

Section: Park Et Al (2010) Use Low-level Sensor Events As Observatiomentioning

confidence: 99%

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Contextualized behavior patterns for change reasoning in Ambient Assisted Living: A formal model

2017

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International audienceAmbient Assisted Living, smart environments and technology used for elder care, increases independent living time and cuts long-term care costs. An important requirement for these systems is detecting and informing about abnormal behavior in users' routines. Still, routines may change when certain context conditions hold, which does not mean these changes should concern caregivers. However, changes due to context conditions are not often considered in current proposals. In this paper, we formalize the main concepts related to activities in AAL and we introduce contextualized behavior patterns, a long-term behavior model that considers variability due to context. We adopt a semantic similarity that allows to better detect behavior changes and to understand what makes an observation of daily living different from expected patterns. The results of the experience with three public data sets are promising. Keywords: behavior analysis · behavioral patterns · activities of daily living · elder care · context awareness · long term behavior modelin

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“…IoT enables different smart environments with various smart devices [3][4] such as smart homes, smart buildings, smart communities, smart cities, and smart grids. IoT also enables numerous applications, including healthcare [4][5] [6], home automation [7], security [8][9] [10], and surveillance [11] [12][13] [14]. Among these applications, healthcare attracts a lot of attention in recent years [4] [15] due to the growth of the elderly population.…”

Section: Introductionmentioning

confidence: 99%

Privacy Mining from IoT-Based Smart Homes

Lee

Lin

Owe

2018

Lecture Notes on Data Engineering and Communications Technologies

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Recently, a wide range of smart devices are deployed in a variety of environments to improve the quality of human life. One of the important IoTbased applications is smart homes for healthcare, especially for elders. IoTbased smart homes enable elders' health to be properly monitored and taken care of. However, elders' privacy might be disclosed from smart homes due to non-fully protected network communication or other reasons. To demonstrate how serious this issue is, we introduce in this paper a Privacy Mining Approach (PMA) to mine privacy from smart homes by conducting a series of deductions and analyses on sensor datasets generated by smart homes. The experimental results demonstrate that PMA is able to deduce a global sensor topology for a smart home and disclose elders' privacy in terms of their house layouts.

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Detecting and exploring deviating behaviour of smart home residents

Cited by 38 publications

References 21 publications

A Literature Review on the Design of Smart Homes for People with Dementia Using a User-Centred Design Approach

A Literature Review on the Design of Smart Homes for People with Dementia Using a User-Centred Design Approach

Contextualized behavior patterns for change reasoning in Ambient Assisted Living: A formal model

Privacy Mining from IoT-Based Smart Homes

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