In-home monitoring system based on WiFi fingerprints for ambient assisted living

Torres-Sospedra, Joaquín; Trilles, Sergio; Calia, Andrea; Belmonte-Fernández, Óscar; Montoliu, Raúl

doi:10.3233/ais-170450

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…The existing floor positioning methods have some limitations. For example, the deployment requirements of the WiFi signal source are high, and a certain number of signal sources need to be deployed on each floor, or need to establish WiFi fingerprint database (Yungeun et al 2013), geomagnetic fingerprint database offline, etc., and high dependence on the signal environment (Joaquin et al 2017). Once the support of the positioning hardware infrastructure is lost or the fingerprint database missing, the floor positioning cannot be effectively performed.…”

Section: Introductionmentioning

confidence: 99%

Floor positioning method indoors with smartphone’s barometer

Xue

Ruan

et al. 2019

Geo-spatial Information Science

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This paper presents an indoor floor positioning method with the smartphone's barometer for the purpose of solving the problem of low availability and high environmental dependence of the traditional floor positioning technology. First, an initial floor position algorithm with the "entering" detection algorithm has been obtained. Second, the user's going upstairs or downstairs activities are identified by the characteristics of the air pressure fluctuation. Third, the moving distance in the vertical direction and the floor change during going upstairs or downstairs are estimated to obtain the accurate floor position. In order to solve the problem of the floor misjudgment from different mobile phone's barometers, this paper calculates the pressure data from the different cell phones, and effectively reduce the errors of the air pressure estimating the elevation which is caused by the heterogeneity of the mobile phones. The experiment results show that the average correct rate of the floor identification is more than 85% for three types of the cell phones while reducing environmental dependence and improving availability. Further, this paper compares and analyzes the three common floor location methodsthe WLAN Floor Location (WFL) method based on the fingerprint, the Neural Network Floor Location (NFL) methods, and the Magnetic Floor Location (MFL) method with our method. The experiment results achieve 94.2% correct rate of the floor identification with Huawei mate10 Pro mobile phone.

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Section: Introductionmentioning

confidence: 99%

Floor positioning method indoors with smartphone’s barometer

Xue

Ruan

et al. 2019

Geo-spatial Information Science

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Comparative Study of Different BLE Fingerprint Reconstruction Techniques

Parralejo

Aranda

Paredes

et al. 2021

2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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An unsupervised anomaly detection framework for smart assisted living via growing neural gas networks

Ciprian,

Gadaleta,

Rossi

2024

AIS

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In this study, we present a novel framework for detecting anomalies in everyday activities within a smart-home environment. Our method utilizes the growing neural gas (GNG) concept to dynamically adapt to the changing behaviors of monitored individuals, eliminating the need for supervised input. To develop and evaluate our framework, we collected real-life data from environmental sensors that tracked the daily activities of 17 elderly subjects over a continuous two-year period. The proposed approach is highly versatile, capable of detecting a wide range of anomalies associated with daily living activities. We focus on activities that exhibit abnormal duration, frequency, or entirely new behaviors that deviate from established routines. The performance evaluation of our framework revolves around two key aspects: reliability and adaptability. Reliability measures the accuracy of detecting unusual events, while adaptability assesses the system’s ability to accommodate changes in user behavior. This involves recognizing recurrent anomalous behaviors as new norms over time and transitioning from persistent anomalies during an initial phase. Our proposed anomaly detection system demonstrates promising results in real-life scenarios. It achieves good reliability, with true negative rate and true positive rate exceeding 90% and 80% respectively, across all activities and users. Additionally, the system swiftly adapts to new individuals or their evolving behaviors, adjusting within a span of 3 to 7 days for new behaviors.

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In-home monitoring system based on WiFi fingerprints for ambient assisted living

Cited by 3 publications

References 31 publications

Floor positioning method indoors with smartphone’s barometer

Floor positioning method indoors with smartphone’s barometer

Comparative Study of Different BLE Fingerprint Reconstruction Techniques

An unsupervised anomaly detection framework for smart assisted living via growing neural gas networks

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