An Ambient Assisted-Living Architecture Based on Wireless Sensor Networks

Andréu, Javier; Viúdez, Jaime; Holgado, Juan A.

doi:10.1007/978-3-540-85867-6_28

Cited by 12 publications

(5 citation statements)

References 9 publications

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“…In previous studies we worked on Ambient Assisted Living (AAL) application using wireless sensors network (WSN) for elderly monitoring [7]. Across our projects we employed a great variety of heterogeneous sensors such as RFID, biosensors, accelerometers, gyroscopes etc.…”

Section: Background and Related Workmentioning

confidence: 98%

Real-time human activity recognition from wireless sensors using evolving fuzzy systems

Andréu

Angelov

2010

International Conference on Fuzzy Systems

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A new approach to real-time knowledge extraction from streaming data generated by wearable wireless accelerometers based on self-learning evolving fuzzy rule-based classifier is proposed and evaluated in this paper. Based on experiments with real subjects we collected data from 18 different classifieds activities. After preprocessing and classifying data depending on the sequence of activities regarding time, we achieved up to 99.81% of accuracy in recognizing a sequence of activities. This technique allows re-training the system as long as the application is running on the wearable intelligent/smart sensor, getting a better classification rate throughout the time without an increase of the delay in performance.

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Section: Background and Related Workmentioning

confidence: 98%

Real-time human activity recognition from wireless sensors using evolving fuzzy systems

Andréu

Angelov

2010

International Conference on Fuzzy Systems

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“…Notable first generation devices applications include daily activity recognition from wearable motion sensors or sensors embedded in the environment [7]; gait analysis from wearable sensors [8] or those embedded in the flooring of a smart house [9]. Whilst the amount of data processed from wearable sensors that can be stored is limited to several megabytes, applications based on video and/or audio signals can generate up to hundred megabytes of data [10].…”

Section: A First Generationmentioning

confidence: 99%

From Wearable Sensors to Smart Implants-–Toward Pervasive and Personalized Healthcare

Andreu-Pérez

Leff

et al. 2015

IEEE Trans. Biomed. Eng.

311

156

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Abstract-Objective:This article discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorised into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multi-omics data integration and social media. Conclusion: Technical advances have supported the evolution of the pervasive health paradigm towards preventative, predictive, personalised and participatory medicine. Significance: The sensing technologies discussed in this paper and their future evolution will play a key role in realising the goal of sustainable healthcare systems.Index Terms-pervasive health, wearable sensors, implantable sensors, health informatics.

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“…There are several works which have studied this solution and some of them tried to make up an ambient intelligent platform considering the AAL requirements [2]. In this line, our latter research [3] discussed the development, design, characterization and tests of miniaturized wireless and wearable sensors both ECG and Fall Detector. In this research we describe how a wireless sensor platform is incorporated with the TAAL (Telecare Ambient Assisted Living).…”

Section: Experiences Of Services-orientation For Aalmentioning

confidence: 99%

Service-Oriented Device Integration for Ubiquitous Ambient Assisted Living Environments

Pérez

Álvarez

Fernández-Montes

et al. 2009

Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living

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As a result of the increment of population in countries of Europe, a lot of efforts from European Authorities are coming from. In our research we want to bring forward a suite of developments related to build a ubiquitous AAL (Ambient Assisted Living) environment. We consider that recent approaches are based on ad-hoc technologies so its application is in this context isolated just in one domain of application. Our approach addresses to a reliable services platform for heterogeneous devices integration. On this basis we want to consider as well, the underlying benefits that a Service-oriented platform is giving to us in our Ambient Assisted Living Applications.

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An Ambient Assisted-Living Architecture Based on Wireless Sensor Networks

Cited by 12 publications

References 9 publications

Real-time human activity recognition from wireless sensors using evolving fuzzy systems

Real-time human activity recognition from wireless sensors using evolving fuzzy systems

From Wearable Sensors to Smart Implants-–Toward Pervasive and Personalized Healthcare

Service-Oriented Device Integration for Ubiquitous Ambient Assisted Living Environments

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