An experimental characterization of reservoir computing in ambient assisted living applications

Bacciu, Davide; Barsocchi, Paolo; Chessa, Stefano; Gallicchio, Claudio; Micheli, Alessio

doi:10.1007/s00521-013-1364-4

Cited by 99 publications

(78 citation statements)

References 37 publications

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“…Indoor User Movement Prediction from RSS Dataset UCI [6] Real-life benchmark in the area of AAL applications. The binary classification introduced predicts the pattern of user movements in real-world environments.…”

Section: Dataset Repository Refs Main Features and Limitationsmentioning

confidence: 99%

Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning

Lohan

Torres-Sospedra

Leppäkoski

et al. 2017

Data

133

104

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Benchmark open-source Wi-Fi fingerprinting datasets for indoor positioning studies are still hard to find in the current literature and existing public repositories. This is unlike other research fields, such as the image processing field, where benchmark test images such as the Lenna image or Face Recognition Technology (FERET) databases exist, or the machine learning field, where huge datasets are available for example at the University of California Irvine (UCI) Machine Learning Repository. It is the purpose of this paper to present a new openly available Wi-Fi fingerprint dataset, comprised of 4648 fingerprints collected with 21 devices in a university building in Tampere, Finland, and to present some benchmark indoor positioning results using these data. The datasets and the benchmarking software are distributed under the open-source MIT license and can be found on the EU Zenodo repository.

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Section: Dataset Repository Refs Main Features and Limitationsmentioning

confidence: 99%

Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning

Lohan

Torres-Sospedra

Leppäkoski

et al. 2017

Data

133

104

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“…Preliminary analysis of the trade-off between efficacy and efficiency of the RC approach in AAL applications were investigated in [37][38][39][40]. Furthermore, preliminary investigations on the predictive performance of ESN modules in human activity recognition applications (targeted in particular to the EVAAL competition scenario) can be found in [41].…”

Section: Learningmentioning

confidence: 99%

Robotic Ubiquitous Cognitive Ecology for Smart Homes

Amato

Bacciu

Broxvall

et al. 2015

J Intell Robot Syst

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Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agentbased control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feedback received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work.

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“…The solutions outlined in the previous section have been evaluated both in isolation and as part of integrated systems (Di, Rocco 2013; Palumbo et al, 2013;Bacciu et al, 2014aBacciu et al, , 2014b. In our previous publications we have discussed the performances of the multi-layered RUBICON approach in a smart home environment.…”

Section: Powering Aal Solutions With Rubiconmentioning

confidence: 99%

A cognitive robotic ecology approach to self-configuring and evolving AAL systems

Dragone

Amato

Bacciu

et al. 2015

Engineering Applications of Artificial Intelligence

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a b s t r a c tRobotic ecologies are systems made out of several robotic devices, including mobile robots, wireless sensors and effectors embedded in everyday environments, where they cooperate to achieve complex tasks. This paper demonstrates how endowing robotic ecologies with information processing algorithms such as perception, learning, planning, and novelty detection can make these systems able to deliver modular, flexible, manageable and dependable Ambient Assisted Living (AAL) solutions. Specifically, we show how the integrated and self-organising cognitive solutions implemented within the EU project RUBICON (Robotic UBIquitous Cognitive Network) can reduce the need of costly pre-programming and maintenance of robotic ecologies. We illustrate how these solutions can be harnessed to (i) deliver a range of assistive services by coordinating the sensing & acting capabilities of heterogeneous devices, (ii) adapt and tune the overall behaviour of the ecology to the preferences and behaviour of its inhabitants, and also (iii) deal with novel events, due to the occurrence of new user's activities and changing user's habits.

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An experimental characterization of reservoir computing in ambient assisted living applications

Cited by 99 publications

References 37 publications

Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning

Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning

Robotic Ubiquitous Cognitive Ecology for Smart Homes

A cognitive robotic ecology approach to self-configuring and evolving AAL systems

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