@conference{DeCaro2023,

title = {Prediction of Driver's Stress Affection in Simulated Autonomous Driving Scenarios},

author = {Valerio De Caro and Herbert Danzinger and Claudio Gallicchio and Clemens Könczöl and Vincenzo Lomonaco and Mina Marmpena and Mina Marpena and Sevasti Politi and Omar Veledar and Davide Bacciu},

year  = {2023},

date = {2023-06-04},

urldate = {2023-06-04},

booktitle = {Proceedings of 2023 IEEE International Conference on Acoustics, Speech and Signal Processing},

abstract = {We investigate the task of predicting stress affection from physiological data of users experiencing simulations of autonomous driving. We approach this task on two levels of granularity, depending on whether the prediction is performed at end of the simulation, or along the simulation. In the former, denoted as coarse-grained prediction, we employed Decision Trees. In the latter, denoted as fine-grained prediction, we employed Echo State Networks, a Recurrent Neural Network

that allows efficient learning from temporal data and hence is

suitable for pervasive environments. We conduct experiments on a private dataset of physiological data from people participating in multiple driving scenarios simulating different stressful events. The results show that the proposed model is capable of detecting conditions of event-related cognitive stress proving, the existence of a correlation between stressful events and the physiological data.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{decaro2022aiasaservice,

title = {AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving},

author = {Valerio De Caro and Saira Bano and Achilles Machumilane and Alberto Gotta and Pietro Cassará and Antonio Carta and Christos Sardianos and Christos Chronis and Iraklis Varlamis and Konstantinos Tserpes and Vincenzo Lomonaco and Claudio Gallicchio and Davide Bacciu},

url = {https://arxiv.org/pdf/2202.01645.pdf, arxiv},

year  = {2022},

date = {2022-03-21},

urldate = {2022-03-21},

booktitle = {Proceedings of the 20th International Conference on Pervasive Computing and Communications (PerCom 2022)},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{BacciuIJCNN2021,

title = {Federated Reservoir Computing Neural Networks},

author = {Davide Bacciu and Daniele Di Sarli and Pouria Faraji and Claudio Gallicchio and Alessio Micheli},

doi = {10.1109/IJCNN52387.2021.9534035},

year  = {2021},

date = {2021-07-18},

urldate = {2021-07-18},

booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021)},

publisher = {IEEE},

abstract = {A critical aspect in Federated Learning is the aggregation strategy for the combination of multiple models, trained on the edge, into a single model that incorporates all the knowledge in the federation. Common Federated Learning approaches for Recurrent Neural Networks (RNNs) do not provide guarantees on the predictive performance of the aggregated model. In this paper we show how the use of Echo State Networks (ESNs), which are efficient state-of-the-art RNN models for time-series processing, enables a form of federation that is optimal in the sense that it produces models mathematically equivalent to the corresponding centralized model. Furthermore, the proposed method is compliant with privacy constraints. The proposed method, which we denote as Incremental Federated Learning, is experimentally evaluated against an averaging strategy on two datasets for human state and activity recognition.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Bacciu2021,

title = {Benchmarking Reservoir and Recurrent Neural Networks for Human State and Activity Recognition},

author = {Davide Bacciu and Daniele Di Sarli and Claudio Gallicchio and Alessio Micheli and Niccolo Puccinelli},

doi = {10.1007/978-3-030-85099-9_14},

year  = {2021},

date = {2021-06-16},

urldate = {2021-06-16},

booktitle = {Proceedings of the 16th International Work Conference on Artificial Neural Networks (IWANN 2021)},

volume = {12862},

pages = {168-179},

publisher = {Springer},

abstract = {Monitoring of human states from streams of sensor data is an appealing applicative area for Recurrent Neural Network (RNN) models. In such a scenario, Echo State Network (ESN) models from the Reservoir Computing paradigm can represent good candidates due to the efficient training algorithms, which, compared to fully trainable RNNs, definitely ease embedding on edge devices.



In this paper, we provide an experimental analysis aimed at assessing the performance of ESNs on tasks of human state and activity recognition, in both shallow and deep setups. Our analysis is conducted in comparison with vanilla RNNs, Long Short-Term Memory, Gated Recurrent Units, and their deep variations. Our empirical results on several datasets clearly indicate that, despite their simplicity, ESNs are able to achieve a level of accuracy that is competitive with those models that require full adaptation of the parameters. From a broader perspective, our analysis also points out that recurrent networks can be a first choice for the class of tasks under consideration, in particular in their deep and gated variants.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{esann2017,

title = {ELM Preference Learning for Physiological Data},

author = {Bacciu Davide and Colombo Michele and Morelli Davide and Plans David},

editor = {Michel Verleysen},

isbn = {978-2-875870384},

year  = {2017},

date = {2017-04-28},

urldate = {2017-04-28},

booktitle = {Proceedings  of the European Symposium on Artificial Neural Networks,  Computational Intelligence and Machine Learning (ESANN'17)},

pages = {99-104},

publisher = {i6doc.com},

address = {Louvain-la-Neuve, Belgium},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{ie2016,

title = { Detecting socialization events in ageing people: the experienze of the DOREMI project},

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

doi = {10.1109/IE.2016.28},

issn = {2472-7571 },

year  = {2016},

date = {2016-10-27},

urldate = {2016-10-27},

booktitle = {Proceedings of the IEEE 12th International Conference on Intelligent Environments (IE 2016), },

pages = {132-135},

publisher = {IEEE},

address = {UK, London},

abstract = {The detection of socialization events is useful to build indicators about social isolation of people, which is an important indicator in e-health applications. On the other hand, it is rather difficult to achieve with non-invasive solutions. This paper reports about the currently work-in-progress on the technological solution for the detection of socialization events adopted in the DOREMI project.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Amato2016,

title = {A Benchmark Dataset for Human Activity Recognition and Ambient Assisted Living},

author = {Amato Giuseppe and Bacciu Davide and Chessa Stefano and Dragone Mauro and Gallicchio Claudio and Gennaro Claudio and Lozano Hector and Micheli Alessio and Renteria Arantxa

and Vairo Claudio},

doi = {10.1007/978-3-319-40114-0_1},

isbn = {978-3-319-40113-3},

year  = {2016},

date = {2016-06-03},

booktitle = {Proceedings of the 7th International Conference on Ambient Intelligence (ISAMI'16)},

volume = {476},

pages = {1-9},

publisher = {Springer},

series = {Advances in Intelligent Systems and Computing},

abstract = {We present a data benchmark for the assessment of human activity recognition solutions, collected as part of the EU FP7 RUBICON project, and available to the scientific community. The dataset provides fully annotated data pertaining to numerous user activities and comprises synchronized data streams collected from a highly sensor-rich home environment. A baseline activity recognition performance obtained through an Echo State Network approach is provided along with the dataset.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_775269,

title = {Smart environments and context-awareness for lifestyle management in a healthy active ageing framework},

author = {Bacciu Davide and Chessa Stefano and Gallicchio Claudio and Micheli Alessio and Ferro Erina and Fortunati Luigi and Palumbo Filippo and Parodi Oberdan and Vozzi Federico and Hanke Sten and Kropf Johannes and Kreiner Karl},

url = {http://springerlink.com/content/0302-9743/copyright/2005/},

doi = {10.1007/978-3-319-23485-4_6},

year  = {2015},

date = {2015-01-01},

booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {9273},

pages = {54--66},

publisher = {Springer Verlag},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466867,

title = {Self-Sustaining Learning for Robotic Ecologies},

author = {BACCIU Davide and Broxvall Mathias and Coleman Sonya and Dragone Mauro and Gallicchio Claudio and Gennaro Claudio and Guzman Roberto and Lopez Raul and Lozano-Peiteado Hector and Ray AK and Renteria Arantxa and Saffiotti Alessandro and Vairo Claudio},

year  = {2012},

date = {2012-01-01},

booktitle = {Proceedings of the 1st International Conference on Sensor Networks, SENSORNETS 2012},

pages = {99--103},

abstract = {The most common use of wireless sensor networks (WSNs) is to collect environmental data from a specific area, and to channel it to a central processing node for on-line or off-line analysis. The WSN technology, however, can be used for much more ambitious goals. We claim that merging the concepts and technology of WSN with the concepts and technology of distributed robotics and multi-agent systems can open new ways to design systems able to provide intelligent services in our homes and working places. We also claim that endowing these systems with learning capabilities can greatly increase their viability and acceptability, by simplifying design, customization and adaptation to changing user needs. To support these claims, we illustrate our architecture for an adaptive robotic ecology, named RUBICON, consisting of a network of sensors, effectors and mobile robots.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_202140,

title = {Predicting User Movements in Heterogeneous Indoor Environments by Reservoir Computing},

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

url = {http://ijcai-11.iiia.csic.es/files/proceedings/Space,%20Time%20and%20Ambient%20Intelligence%20Proceeding.pdf},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

booktitle = {Proceedings of the IJCAI Workshop on Space, Time and Ambient Intelligence (STAMI)},

pages = {1--6},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}