@article{rubicon2019CI,

title = {An Ambient Intelligence Approach for Learning in Smart Robotic Environments},

author = {Bacciu Davide and Di Rocco Maurizio and Dragone Mauro and Gallicchio Claudio and Micheli Alessio and Saffiotti Alessandro},

doi = {10.1111/coin.12233},

year  = {2019},

date = {2019-07-31},

journal = {Computational Intelligence},

abstract = {Smart robotic environments combine traditional (ambient) sensing devices and mobile robots. This combination extends the type of applications that can be considered, reduces their complexity, and enhances the individual values of the devices involved by enabling new services that cannot be performed by a single device. In order to reduce the amount of preparation and pre-programming required for their deployment in real world applications,  it is important to make these systems self-learning, self-configuring, and self-adapting. The solution presented in this paper is based upon a type of compositional adaptation where (possibly multiple) plans of actions are created through planning and involve the activation of pre-existing capabilities. All the devices in the smart environment  participate in a pervasive learning infrastructure, which is exploited to recognize which plans of actions are most suited to the current situation. The system is evaluated in experiments run in a real domestic environment, showing its ability to pro-actively and smoothly adapt to subtle changes in the environment and in the habits and preferences

of their user(s).},

note = {Early View (Online Version of Record before inclusion in an issue)

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{tnnnls_dropin2019,

title = {Augmenting Recurrent Neural Networks Resilience by Dropout},

author = {Davide Bacciu and Francesco Crecchi },

doi = {10.1109/TNNLS.2019.2899744},

year  = {2019},

date = {2019-03-31},

urldate = {2019-03-31},

journal = {IEEE Transactions on Neural Networs and Learning Systems},

abstract = {The paper discusses the simple idea that dropout regularization can be used to efficiently induce resiliency to missing inputs at prediction time in a generic neural network.  We show how the approach can be effective on tasks where imputation strategies often fail, namely involving recurrent neural networks and scenarios where whole sequences of input observations are missing. The experimental analysis provides an assessment of the accuracy-resiliency tradeoff in multiple recurrent models, including reservoir computing methods, and comprising real-world ambient intelligence and biomedical time series.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{eaai2017,

title = {A Learning System for Automatic Berg Balance Scale Score Estimation},

author = {Bacciu Davide and Chessa Stefano and Gallicchio Claudio and Micheli Alessio and Pedrelli Luca and Ferro Erina and Fortunati Luigi and La Rosa Davide and Palumbo Filippo and Vozzi Federico and Parodi Oberdan},

url = {http://www.sciencedirect.com/science/article/pii/S0952197617302026},

doi = {https://doi.org/10.1016/j.engappai.2017.08.018},

year  = {2017},

date = {2017-08-24},

urldate = {2017-08-24},

journal = {Engineering Applications of Artificial Intelligence journal},

volume = {66},

pages = {60-74},

abstract = {The objective of this work is the development of a learning system for the automatic assessment of balance abilities in elderly people. The system is based on estimating the Berg Balance Scale (BBS) score from the stream of sensor data gathered by a Wii Balance Board. The scientific challenge tackled by our investigation is to assess the feasibility of exploiting the richness of the temporal signals gathered by the balance board for inferring the complete BBS score based on data from a single BBS exercise.



The relation between the data collected by the balance board and the BBS score is inferred by neural networks for temporal data, modeled in particular as Echo State Networks within the Reservoir Computing (RC) paradigm, as a result of a comprehensive comparison among different learning models. The proposed system results to be able to estimate the complete BBS score directly from temporal data on exercise #10 of the BBS test, with ≈≈10 s of duration. Experimental results on real-world data show an absolute error below 4 BBS score points (i.e. below the 7% of the whole BBS range), resulting in a favorable trade-off between predictive performance and user’s required time with respect to previous works in literature. Results achieved by RC models compare well also with respect to different related learning models.



Overall, the proposed system puts forward as an effective tool for an accurate automated assessment of balance abilities in the elderly and it is characterized by being unobtrusive, easy to use and suitable for autonomous usage.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jrie2017,

title = {Reliability and human factors in Ambient Assisted Living environments: The DOREMI case study},

author = {Palumbo Filippo and La Rosa Davide and Ferro Erina and Bacciu Davide and Gallicchio Claudio and Micheli Alession and Chessa Stefano and Vozzi Federico and Parodi Oberdan},

doi = {10.1007/s40860-017-0042-1},

isbn = {2199-4668},

year  = {2017},

date = {2017-06-17},

journal = {Journal of Reliable Intelligent Environments},

volume = {3},

number = {3},

pages = {139–157},

publisher = {Springer},

abstract = {Malnutrition, sedentariness, and cognitive decline in elderly people represent the target areas addressed by the DOREMI project. It aimed at developing a systemic solution for elderly, able to prolong their functional and cognitive capacity by empowering, stimulating, and unobtrusively monitoring the daily activities according to well-defined “Active Ageing” life-style protocols. Besides the key features of DOREMI in terms of technological and medical protocol solutions, this work is focused on the analysis of the impact of such a solution on the daily life of users and how the users’ behaviour modifies the expected results of the system in a long-term perspective. To this end, we analyse the reliability of the whole system in terms of human factors and their effects on the reliability requirements identified before starting the experimentation in the pilot sites. After giving an overview of the technological solutions we adopted in the project, this paper concentrates on the activities conducted during the two pilot site studies (32 test sites across UK and Italy), the users’ experience of the entire system, and how human factors influenced its overall reliability.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{icfNca15,

title = {Unsupervised feature selection for sensor time-series in pervasive computing applications},

author = {Bacciu Davide},

url = {http://pages.di.unipi.it/bacciu/wp-content/uploads/sites/12/2016/04/nca2015.pdf},

doi = {10.1007/s00521-015-1924-x},

issn = {1433-3058},

year  = {2016},

date = {2016-07-01},

urldate = {2016-07-01},

journal = {Neural Computing and Applications},

volume = {27},

number = {5},

pages = {1077-1091},

publisher = {Springer London},

abstract = {The paper introduces an efficient feature selection approach for multivariate time-series of heterogeneous sensor data within a pervasive computing scenario. An iterative filtering procedure is devised to reduce information redundancy measured in terms of time-series cross-correlation. The algorithm is capable of identifying nonredundant sensor sources in an unsupervised fashion even in presence of a large proportion of noisy features. In particular, the proposed feature selection process does not require expert intervention to determine the number of selected features, which is a key advancement with respect to time-series filters in the literature. The characteristic of the prosed algorithm allows enriching learning systems, in pervasive computing applications, with a fully automatized feature selection mechanism which can be triggered and performed at run time during system operation. A comparative experimental analysis on real-world data from three pervasive computing applications is provided, showing that the algorithm addresses major limitations of unsupervised filters in the literature when dealing with sensor time-series. Specifically, it is presented an assessment both in terms of reduction of time-series redundancy and in terms of preservation of informative features with respect to associated supervised learning tasks.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Dragone:2015:CRE:2827370.2827596,

title = {A Cognitive Robotic Ecology Approach to Self-configuring and Evolving AAL Systems},

author = {Dragone Mauro and Amato Giuseppe and Bacciu Davide and Chessa Stefano and Coleman Sonya and Di Rocco Maurizio and Gallicchio Claudio and Gennaro Claudio and Lozano Hector and Maguire Liam and McGinnity Martin and Micheli Alessio and O'Hare Gregory M.P. and Renteria Arantxa and Saffiotti Alessandro and Vairo Claudio and Vance Philip},

url = {http://dx.doi.org/10.1016/j.engappai.2015.07.004},

doi = {10.1016/j.engappai.2015.07.004},

issn = {0952-1976},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Engineering Applications of Artificial Intelligence},

volume = {45},

number = {C},

pages = {269--280},

publisher = {Pergamon Press, Inc.},

address = {Tarrytown, NY, USA},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bacciuJirs15,

title = {Robotic Ubiquitous Cognitive Ecology for Smart Homes},

author = {Amato Giuseppe and Bacciu Davide and Broxvall Mathias and Chessa Stefano and Coleman Sonya and Di Rocco Maurizio and Dragone Mauro and Gallicchio Claudio and Gennaro Claudio and Lozano Hector and McGinnity T Martin and Micheli Alessio and Ray AK and Renteria Arantxa and Saffiotti Alessandro and Swords David and Vairo Claudio and Vance Philip},

url = {http://dx.doi.org/10.1007/s10846-015-0178-2},

doi = {10.1007/s10846-015-0178-2},

issn = {0921-0296},

year  = {2015},

date = {2015-01-01},

journal = {Journal of Intelligent & Robotic Systems},

volume = {80},

number = {1},

pages = {57-81},

publisher = {Springer Netherlands},

abstract = {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 agent-based 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 proof of concept 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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nca2014,

title = {An experimental characterization of reservoir computing in ambient assisted living applications},

author = {Bacciu Davide and Barsocchi Paolo and Chessa Stefano and Gallicchio Claudio and Micheli Alessio},

url = {http://dx.doi.org/10.1007/s00521-013-1364-4, Publisher version

https://archive.ics.uci.edu/ml/datasets/Indoor+User+Movement+Prediction+from+RSS+data, Dataset @ UCI},

doi = {10.1007/s00521-013-1364-4},

issn = {0941-0643},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

journal = {Neural Computing and Applications},

volume = {24},

number = {6},

pages = {1451-1464},

publisher = {Springer London},

abstract = {In this paper, we present an introduction and critical experimental evaluation of a reservoir computing (RC) approach for ambient assisted living (AAL) applications. Such an empirical analysis jointly addresses the issues of efficiency, by analyzing different system configurations toward the embedding into computationally constrained wireless sensor devices, and of efficacy, by analyzing the predictive performance on real-world applications. First, the approach is assessed on a validation scheme where training, validation and test data are sampled in homogeneous ambient conditions, i.e., from the same set of rooms. Then, it is introduced an external test set involving a new setting, i.e., a novel ambient, which was not available in the first phase of model training and validation. The specific test-bed considered in the paper allows us to investigate the capability of the RC approach to discriminate among user movement trajectories from received signal strength indicator sensor signals. This capability can be exploited in various AAL applications targeted at learning user indoor habits, such as in the proposed indoor movement forecasting task. Such a joint analysis of the efficiency/efficacy trade-off provides novel insight in the concrete successful exploitation of RC for AAL tasks and for their distributed implementation into wireless sensor networks.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}