Anomaly Detection in Elderly Daily Behavior in Ambient Sensing Environments

Aran, Oya; Sanchez-Cortes, Dairazalia; Do, Minh-Tri; Gática-Pérez, Daniel

doi:10.1007/978-3-319-46843-3_4

Cited by 66 publications

(47 citation statements)

References 28 publications

(34 reference statements)

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“…Aran et al [106] take a similar approach to the one proposed by Lotfi et al Like Lotfi's work, Aran infers the current location of a smart home resident and uses k means clustering to group similar data points based on time of day, location, and time spent in the location. The clustering algorithm provides a convenient basis for determining outliers as data points that do not fit well in any of the existing clusters.…”

Section: Detecting and Assessing Threatsmentioning

confidence: 99%

Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats

Dahmen

Cook

Wang

et al. 2017

J Reliable Intell Environ

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Smart home design has undergone a metamorphosis in recent years. The field has evolved from designing theoretical smart home frameworks and performing scripted tasks in laboratories. Instead, we now find robust smart home technologies that are commonly used by large segments of the population in a variety of settings. Recent smart home applications are focused on activity recognition, health monitoring, and automation. In this paper, we take a look at another important role for smart homes: security. We first explore the numerous ways smart homes can and do provide protection for their residents. Next, we provide a comparative analysis of the alternative tools and research that has been developed for this purpose. We investigate not only existing commercial products that have been introduced but also discuss the numerous research that has been focused on detecting and identifying potential threats. Finally, we close with open challenges and ideas for future research that will keep individuals secure and healthy while in their own homes.

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Section: Detecting and Assessing Threatsmentioning

confidence: 99%

Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats

Dahmen

Cook

Wang

et al. 2017

J Reliable Intell Environ

View full text Add to dashboard Cite

show abstract

“…Zhao et al used a novel computer-vision-based system consisting of inexpensive programmable depth sensors, wearable devices, and smart phones to ensure worker safety in the workplace without violating their privacy [14]. Aran et al proposed an indicator of predictability based on the cross-entropy measure for the detection of anomalies in elderly daily behavior [15]. Alcalá et al used a novel approach to identify appliance activities from smart meter data and extract the pattern of usage, which is used to monitor the health of the household's occupant [16].…”

Section: Related Workmentioning

confidence: 99%

A Visual Analytics Approach for Detecting and Understanding Anomalous Resident Behaviors in Smart Healthcare

et al. 2017

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Abstract:With the development of science and technology, it is possible to analyze residents' daily behaviors for the purpose of smart healthcare in the smart home environment. Many researchers have begun to detect residents' anomalous behaviors and assess their physical condition, but these approaches used by the researchers are often caught in plight caused by a lack of ground truth, one-sided analysis of behavior, and difficulty of understanding behaviors. In this paper, we put forward a smart home visual analysis system (SHVis) to help analysts detect and comprehend unusual behaviors of residents, and predict the health information intelligently. Firstly, the system classifies daily activities recorded by sensor devices in smart home environment into different categories, and discovers unusual behavior patterns of residents living in this environment by using various characteristics extracted from those activities and appropriate unsupervised anomaly detection algorithm. Secondly, on the basis of figuring out the residents' anomaly degree of every date, we explore the daily behavior patterns and details with the help of several visualization views, and compare and analyze residents' activities of various dates to find the reasons why residents act unusually. In the case study of this paper, we analyze residents' behaviors that happened over two months and find unusual indoor behaviors and give health advice to the residents.

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“…However, this also leads to privacy issues as well as creates high dependency to people (i.e., users have to carry these devices all the time). Another method includes using ambient sensors [11,12], e.g., infrared or ultrasonic sensors, but the outcomes of these can be highly unreliable due to sensor quality. A recent study [1] showed that activity detection of a single person can be achieved with ambient sensors.…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Presence Detection and Position Tracking for Multiple People Using Low-Resolution Thermal Sensors

Singh

Akşanlı

2019

JSAN

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This paper presents a framework to accurately and non-intrusively detect the number of people in an environment and track their positions. Different from most of the previous studies, our system setup uses only ambient thermal sensors with low-resolution, using no multimedia resources or wearable sensors. This preserves user privacy in the environment, and requires no active participation by the users, causing no discomfort. We first develop multiple methods to estimate the number of people in the environment. Our experiments demonstrate that algorithm selection is very important, but with careful selection, we can obtain up to 100% accuracy when detecting user presence. In addition, we prove that sensor placement plays a crucial role in the system performance, where placing the sensor on the room ceiling yields to the best results. After accurately finding the number of people in the environment, we perform position tracking on the collected ambient data, which are thermal images of the space where there are multiple people. We consider position tracking as static activity detection, where the user's position does not change while performing activities, such as sitting, standing, etc. We perform efficient pre-processing on the data, including normalization and resizing, and then feed the data into well-known machine learning methods. We tested the efficiency of our framework (including the hardware and software setup) by detecting four static activities. Our results show that we can achieved up to 97.5% accuracy when detecting these static activities, with up to 100% class-wise precision and recall rates. Our framework can be very beneficial to several applications such as health-care, surveillance, and home automation, without causing any discomfort or privacy issues for the users.

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Anomaly Detection in Elderly Daily Behavior in Ambient Sensing Environments

Cited by 66 publications

References 28 publications

Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats

Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats

A Visual Analytics Approach for Detecting and Understanding Anomalous Resident Behaviors in Smart Healthcare

Non-Intrusive Presence Detection and Position Tracking for Multiple People Using Low-Resolution Thermal Sensors

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