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Novel Deep Learning (DL) algorithms show ever-increasing accuracy and precision in multiple application domains. However, some steps further are needed towards the ubiquitous adoption of this kind of instrument. First, effort and skills required to develop new DL models, or to adapt existing ones to new use-cases, are hardly available for small- and medium-sized businesses. Second, DL inference must be brought at the edge, to overcome limitations posed by the classically-used cloud computing paradigm. This requires implementation on low-energy computing nodes, often heterogenous and parallel, that are usually more complex to program and to manage. This work describes the ALOHA framework, that proposes a solution to these issue by means of an integrated tool flow that automates most phases of the development process. The framework introduces architecture-awareness, considering the target inference platform very early, already during algorithm selection, and driving the optimal porting of the resulting embedded application. Moreover it considers security, power efficiency and adaptiveness as main objectives during the whole development process.

Although hospital hygiene has a long history in Italy it is necessary to reflect about it because of the innovation in healthcare systems and because of the evolution due to European Union related activities. Different traditions exist in European countries about hospital hygiene and European Centre for Disease Prevention and Control (ECDC) adopted the term of "infection control and hospital hygiene" which includes all the engaged European healthcare professionals. We propose to modify hospital hygiene as "healthcare organisation hygiene" in order to focalise the attention to all care settings not only hospitals and to adopt the following definition: "all activities aimed to guarantee, in an appropriate, scientifically sound and efficient way, that structures and processes support healthcare practices in a safe comfortable and healthy environment both for patients, caregivers and healthcare workers". Hospital hygiene and its professionals, besides the long tradition, still remain a relevant pillar in guaranteeing quality and safety of healthcare in Italy.

Questo documento ha lo scopo di presentare i risultati dei test dei sistemi di monitoraggio di lungo periodo (come specificato nelle attività dell'OO5), con particolare riferimento agli algoritmi per l'identificazione degli indicatori di salute e benessere derivati dai monitoraggi di breve periodo che hanno permesso di effettuare un'analisi su lungo periodo per i soggetti volontari coinvolti. Inoltre, si presentano i dettagli della valutazione sperimentale del servizio di monitoraggio nutrizionale e composizione corporea, dei fattori di stress e delle interazioni sociali.

Cyber-Physical Systems (CPS) are embedded computational collaborating devices, capable of sensing and controlling physical elements and, often, responding to humans. Designing and managing systems able to respond to different, concurrent requirements during operation is not straightforward, and introduce the need of proper support at design-time and run-time. The Cross-layer modEl-based fRamework for multi-oBjective dEsign of Reconfigurable systems in unceRtain hybRid envirOnments (CERBERO) EU project has developed a design environment for adaptive CPS. CERBERO approach leverages on model-based methodologies including different technologies and tools developed to cover design and operation from user interactions down to low level computing layer implementation.

In questo documento sono riportate le attività svolte nell'ambito dell'OO4 durante il secondo anno del progetto INTESA, mirate alla finalizzazione del sistema integrato di monitoraggio della qualità e durata del sonno. Durante questo periodo, partendo dall'architettura del sistema definita nel precedente documento D4.1.1, si è conclusa l'attività di sviluppo e sono stati effettuati i test per la verifica delle funzionalità del sistema e l'integrazione con gli altri componenti della piattaforma INTESA.

In questo documento si descrive la fase di validazione e sperimentazione del dimostratore su utenti reali. ln particolare, si descrivono le varie fasi della sperimentazione e le performance ottenute dai vari sistemi di monitoraggio ed analisi di lungo periodo sui dati raccolti nel periodo di sperimentazione, che include la assistenza agli utenti primari reali e l'interazione con utenti secondari (caregiver).

This document represents the deliverable D4.4 ("Dynamic DSS for an Intelligent Coach"). It shows the advances in the implementation of the DSS concerning the results already shown in D4.2, where the basics of the DSS, its workflows and algorithms of the first prototype were explained. In Chapter 1, we introduce the architecture of the Decision Support System (DSS) and its modules. In Chapter 2, we present the DSS as a system encompassing multiple workflows, which are explained in detail in Chapter 3. Chapter 4 contains a brief explanation of the modules that form the DSS. Last but not least, Chapter 5 addresses the code generated. In order to preserve the Intellectual Property generated in these tasks, we have split this document in two parts: a public and a private document. We encourage the reader to ask for the private version of D4.4. to get the complete understanding of the technical development carried out in WP4; this public version only outlines what has been realised in terms of workflows, but does not go in depth into algorithms and actual implementation of the different modules.

This volume contains the Work-in-Progress papers presented at IPIN 2019, the Tenth International Conference on Indoor Positioning and Indoor Navigation (http://ipin2019.isti.cnr.it/posters) held in Pisa, Italy, on September 30th-October 3rd, 2019.

Present document aims to describe in detail the NESTORE ecosystem architecture and explains its technical specifications on both the implementation criteria and the requirements. NESTORE adopt an evolutionary architecture approach: "An evolutionary architecture designs for incremental change in an architecture as a first principle. Evolutionary architectures are appealing because change has historically been difficult to anticipate and expensive to retrofit. If evolutionary change is built into the architecture, change becomes easier and cheaper, allowing changes to development practices, release practices, and overall agility".

The D4.2 is the software that conforms the first prototype of the Decision Support System. As the DSS is merely designed and implemented in form of a Software as a Service platform, and it does not have any graphical user interface, this document is intended to report a description of the DSS first prototype main features with a particular focus on the architecture, functionalities and technical implementation. Therefore, the aim of this document is to provide a picture of the actual development of the DSS starting from the scientific background from which it is grounded and going through the different elements that form the DSS. The DSS main objective is to help users in selecting coaching plans by proposing personalised recommendations based on users' behaviours and preferences. Recognising such behaviours and their evolution over time is therefore a crucial element for tailoring the interaction of the system with the user. A three-layer system composed of pathways, coaching activity plans, and coaching events, constitutes the so-called coaching timeline on which the analysis is grounded. Various techniques are used to model and personalise the recommendations and feedback. Firstly, the indicators are extracted from disparate data sources, then these are modelled through a profiling system and, finally, recommendations on the pathways and coaching plans are performed through a tagging system. With the aim of developing and testing the models and workflow prior to the pilot starting date, two simulators are also being implemented and reported in this document.

This document represents the deliverable D4.3 (Plans and recommendations based on profiles) of WP4 (NESTORE Decision Support System) and contains a description of the research carried out in the context of task 4.3 "Algorithms for modelling and profiling individuals". In order to preserve the Intellectual Property generated in this task, we have split the D4.3. in two parts: a public and a private document. We encourage the reader to ask for the private version of D4.3. to get the complete understanding of the development carried out in D4.3.; this public version only outlines what has been done. The purpose of this report is to: - characterize NESTORE users through Personas to create end-users' models; - explain the scope of user profiling implementation in the DSS; - demonstrate its functionality with examples; - present the methodology followed; - describe the personalization through recommendations; - explain users' models; - present the objectives and implementation details of a profiler simulator. In chapter 1, we introduce the Decision Support System and its main components with the aim of presenting the user profiling as the main element of personalization. In chapter 2, the user profiling process that has been carried out is listed. In chapter 3, we describe why and how Personas are designed going through all domains of NESTORE interest, and we present the approach we followed to integrate this concept in the profiling system. Chapter 4 introduces and explains the tagging system, which is the core of the recommendation system. In chapter 5, we introduce the recommendations proposed by domain experts and depict their integration into the system. Finally, chapter 6 describes how users are modelled and simulated.

This report contains the description of the metrics and indicators used by the Decision Support System (DSS) for recommending and stimulating the user during the use of the NESTORE coaching system used to make healthier lifestyle choices. This document collects the outcomes of Task 4.1 - Algorithms for Short-term post-processing and extraction of indicators, whose objective is to extract knowledge from data streams generated by the NESTORE sensors and software applications. This kind of data is continuously mined to extract indicators about the NESTORE target domains identified in the WP2 activities, namely physiological, nutritional, cognitive and mental status and social behaviour of the user.

Il documento contiene l'analisi dei requisiti del progetto e la definizione dei parametri biometrici/biomeccanici da rilevare, della piattaforma sensoristica necessaria, dell'interfaccia utente e il database per la memorizzazione delle informazioni.

In questo documento sono riportate le attività svolte nell'ambito dell'OO4 durante il secondo anno del progetto INTESA. In questo periodo, partendo dall'architettura del sistema definita nel precedente documento D4.4.1, si è conclusa l'attività di sviluppo e sono stati effettuati i test per la verifica delle funzionalità del sistema e l'integrazione con gli altri componenti della piattaforma INTESA. Il sistema è stato installato con successo presso la RSA ed è rimasto attivo durante tutto il periodo di sperimentazione permettendo agli operatori ed al personale medico di attuare gli esercizi proposti dal protocollo INTESA con i soggetti partecipanti e fornendo ai servizi di monitoraggio di lungo periodo le informazioni raffinate previste.

Lo scopo di questo documento è la rassegna dello stato dell'arte delle tecniche per l'analisi del cammino, in particolare per l'estrazione degli indicatori individuati nel precedente documento D1.1. Inoltre, considerando i sensori selezionati per l'installazione nella scarpa, sono illustrate le metodologie per adattare gli algoritmi disponibili in letteratura al contesto di Ki-Foot.

This document extends the deliverable D3.2.1 - Environmental Wireless Sensor Network (WSN) prototypes describing: i) the outcomes of the final iteration of the sensors selection for developing the environmental monitoring system of NESTORE; ii) the integrated system tests on the selected sensors. The selection followed the recommendations coming from the WP2 activities in terms of needed monitoring variables and unobtrusiveness. The document also presents the chosen technologies and their integration in the system using available off-the-shelf and custom devices by means of the Web of Things paradigm.