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

Dahmen, Jessamyn; Cook, Diane J.; Wang, Xiaobo; Wang, Honglei

doi:10.1007/s40860-017-0035-0

Cited by 44 publications

(23 citation statements)

References 105 publications

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“…Stattdessen wurde die Personenerkennung so angepasst, dass sie die verzerrten Bilder direkt verarbeiten kann. (Dahmen, Cook, Wang, & Honglei, 2017). Aufgrund der Offenheit gegenüber der konkreten Ausprägung einer Verhaltensabweichung eignet sich dieser Weg besser für das Erkennen von veränderten Tagesabläufen.…”

Section: Personendetektionunclassified

AUXILIA: Nutzerzentriertes Assistenz- und Sicherheitssystem zur Unterstützung von Menschen mit Demenz auf Basis intelligenter Verhaltensanalyse

Seidel

Apitzsch

et al. 2018

awIC

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Durch den demographischen Wandel in Verbindung mit fehlendem Pflegepersonal gewinnt die Entwicklung technischer Assistenzsysteme im Feld des Active and Assisted Living (AAL) zunehmend an Bedeutung. Zukunftsorientierte Instrumente, die in der Altenpﬂege zum Einsatz kommen, sind neuartige, nutzerzentrierte Gesundheitssysteme für Personengruppen mit speziellen Anforderungen. In diesem Beitrag werden das Konzept sowie erste Ergebnisse des Projekts AUXILIA gezeigt, welches ein nutzerzentriertes Assistenz- und Sicherheitssystem zur Unterstützung von Menschen mit Demenz auf Basis intelligenter Verhaltensanalyse beschreibt. Das Assistenz- und Sicherheitssystem richtet sich vor allem an Patienten mit beginnender Demenz. Das Projektziel ist der längere Verbleib von Demenzpatienten in ihren eigenen vier Wänden. Das System unterstützt nicht nur die Patienten, sondern richtet sich darüber hinaus an Nutzer aus dem Pﬂegeumfeld sowie an Angehörige. Mithilfe einer neuartigen Smart Sensor Technologie, die es ermöglicht, den gesamten Raum zu erfassen, können menschliche Verhaltensmuster bestimmt und an die Nutzergruppen weitergegeben werden. Basierend auf diversen Sensordaten werden Objekte kontextbasiert und selbstständig vom System erkannt und daraus Sicherheits- und Assistenzfunktionen, wie beispielsweise eine Sturzdetektion, abgeleitet. Neben der Entwicklung des Sensors wird untersucht, welchen Anforderungen eine Mensch-Maschine-Schnittstelle genügen muss, um für alle Nutzergruppen einen Mehrwert zu bieten.

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Section: Personendetektionunclassified

AUXILIA: Nutzerzentriertes Assistenz- und Sicherheitssystem zur Unterstützung von Menschen mit Demenz auf Basis intelligenter Verhaltensanalyse

Seidel

Apitzsch

et al. 2018

awIC

View full text Add to dashboard Cite

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“…Automated home security systems have been developed using smart home technologies. Recent home security systems protect residents and their properties, making them safe from intruders as conventional security systems, and they enable the detection of risks to the residents in advance through context awareness of the home environment [5,7].…”

Section: Introductionmentioning

confidence: 99%

“…The reason is that if a person's position can be estimated and tracked using microphones and loudspeakers, the entry of an unauthorized In such a smart home environment, microphones are used for context awareness and health monitoring owing to their advantage of operating with low power [4]. Dahmen et al explained that a microphone can be used to identify the scenario of a home environment based on unusual loud noise and the sound of a human falling [5]. In addition, a study explored the possibility of personal identification through footsteps [6].…”

Section: Introductionmentioning

confidence: 99%

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Acoustic-Based Position Estimation of an Object and a Person Using Active Localization and Sound Field Analysis

et al. 2020

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This paper proposes a new method to estimate the position of an object and a silent person with a home security system using a loudspeaker and an array of microphones. The conventional acoustic-based security systems have been developed to detect intruders and estimate the direction of intruders who generate noise. However, there is a need for a method to estimate the distance and angular position of a silent intruder for interoperation with the conventional security sensors, thus overcoming the disadvantage of acoustic-based home security systems, which operate only when sound is generated. Therefore, an active localization method is proposed to estimate the direction and distance of a silent person by actively detecting the sound field variation measured by the microphone array after playing the sound source in the control zone. To implement the idea of the proposed method, two main aspects were studied. Firstly, a signal processing method that estimates the position of a person by the reflected sound, and secondly, the environment in which the proposed method can be operated through a finite-difference time-domain (FDTD) simulation and the acoustic parameters of early decay time (EDT) and reverberation time (RT20). Consequently, we verified that with the proposed method it is possible to estimate the position of a polyvinyl chloride (PVC) pipe and a person by using their reflection in a classroom.

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“…Many novel approaches exist for detecting behavior change; however, their focus is often outside of MVPA, such as investigating smart home sensors for eldercare monitoring and security threats [22,23]. Although other works focus on general change-point detection in time series, they lack the insights necessary for understanding physical activity (PA) data [24].…”

Section: Introductionmentioning

confidence: 99%

Physical Activity Trend eXtraction: A Framework for Extracting Moderate-Vigorous Physical Activity Trends From Wearable Fitness Tracker Data

Faust¹,

Wang²,

Hachen³

et al. 2019

JMIR Mhealth Uhealth

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BackgroundModerate-vigorous physical activity (MVPA) offers extensive health benefits but is neglected by many. As a result, a wide body of research investigating physical activity behavior change has been conducted. As many of these studies transition from paper-based methods of MVPA data collection to fitness trackers, a series of challenges arise in extracting insights from these new data.ObjectiveThe objective of this research was to develop a framework for preprocessing and extracting MVPA trends from wearable fitness tracker data to support MVPA behavior change studies.MethodsUsing heart rate data collected from fitness trackers, we propose Physical Activity Trend eXtraction (PATX), a framework that imputes missing data, recalculates personalized target heart zones, and extracts MVPA trends. We tested our framework on a dataset of 123 college study participants observed across 2 academic years (18 months) using Fitbit Charge HRs. To demonstrate the value of our frameworks’ output in supporting MVPA behavior change studies, we applied it to 2 case studies.ResultsAmong the 123 participants analyzed, PATX labeled 41 participants as experiencing a significant increase in MVPA and 44 participants who experienced a significant decrease in MVPA, with significance defined as P<.05. Our first case study was consistent with previous works investigating the associations between MVPA and mental health. Whereas the second, exploring how individuals perceive their own levels of MVPA relative to their friends, led to a novel observation that individuals were less likely to notice changes in their own MVPA when close ties in their social network mimicked their changes.ConclusionsBy providing meaningful and flexible outputs, PATX alleviates data concerns common with fitness trackers to support MVPA behavior change studies as they shift to more objective assessments of MVPA.

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

Cited by 44 publications

References 105 publications

AUXILIA: Nutzerzentriertes Assistenz- und Sicherheitssystem zur Unterstützung von Menschen mit Demenz auf Basis intelligenter Verhaltensanalyse

AUXILIA: Nutzerzentriertes Assistenz- und Sicherheitssystem zur Unterstützung von Menschen mit Demenz auf Basis intelligenter Verhaltensanalyse

Acoustic-Based Position Estimation of an Object and a Person Using Active Localization and Sound Field Analysis

Physical Activity Trend eXtraction: A Framework for Extracting Moderate-Vigorous Physical Activity Trends From Wearable Fitness Tracker Data

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