NextPerception - Next generation smart perception sensors and distributed intelligence for proactive human monitoring in health, wellbeing, and automotive systems
We put our lives increasingly in the hands of smart complex systems making decisions that directly affect our health and wellbeing. This is very evident in healthcare - where systems watch over your health - as well as in traffic - where autonomous driving solutions are gradually taking over control of the car. The accuracy and timeliness of the decisions depend on the systems’ ability to build a good understanding of both you and your environment, which relies on observations and the ability to reason on them. This project will bring perception sensing technologies like Radar, LiDAR and Time of Flight cameras to the next level, enhancing their features to allow for more accurate detection of human behaviour and physiological parameters. Besides more accurate automotive solutions ensuring driver vigilance and pedestrian and cyclist safety, this innovation will open up new opportunities in health and wellbeing to monitor elderly people at home or unobtrusively assess health state. To facilitate building the complex smart sensing systems envisioned and ensure their secure and reliable operation, the new Distributed Intelligence paradigm will be embraced, enhanced and supported by tools. It leverages the advantages of Edge and Cloud computing, building on the distributed computational resources increasingly available in sensors and edge components to distribute also the intelligence. The goal of this project is to develop next generation smart perception sensors and enhance the distributed intelligence paradigm to build versatile, secure, reliable, and proactive human monitoring solutions for the health, wellbeing, and automotive domains. The project brings together major industrial players and research partners to address top challenges in health, wellbeing, and automotive domains through three use cases: integral vitality monitoring for elderly and exercise, driver monitoring, and providing safety and comfort for vulnerable road users at intersections.
The VALU3S ECSEL Project: Verification and Validation of Automated Systems Safety and Security
In the past years, manufacturers of automated systems and manufacturers of the components used in these systems have been allocating an enormous amount of time and effort in R&D activities, which led to the availability of prototypes demonstrating new capabilities as well as the introduction of such systems to the market within different domains. Manufacturers of these systems need to make sure that the systems function in the intended way and according to specifications which is not a trivial task as system complexity rises dramatically the more integrated and interconnected these systems become with the addition of automated functionality and features to them.
With rising complexity, unknown emerging properties of the system may come to the surface making it necessary to conduct thorough verification and validation (V&V) of these systems. Through the V&V of automated systems, the manufacturers of these systems are able to ensure safe, secure and reliable systems for society to use since failures in highly automated systems can be catastrophic.
The high complexity of automated systems incurs an overhead on the V&V process making it time-consuming and costly. VALU3S aims to design, implement and evaluate state-of-the-art V&V methods and tools in order to reduce the time and cost needed to verify and validate automated systems with respect to safety, cybersecurity and privacy (SCP) requirements. This is a qualification for European manufacturers of automated systems to remain competitive and world leaders in their fields. To this end, a multi-domain framework is designed and evaluated with the aim to create a clear structure around the components and elements needed to conduct V&V process through identification and classification of evaluation methods, tools, environments and concepts that are needed to verify and validate automated systems with respect to SCP requirements.
In VALU3S, 13 use cases with specific safety, security and privacy requirements will be studied in detail. Several state-of-the-art V&V methods will be investigated and further enhanced in addition to implementing new methods aiming for reducing the time and cost needed to conduct V&V of automated systems. The V&V methods investigated are then used to design improved process workflows for V&V of automated systems. Several tools will be implemented supporting the improved processes which are evaluated by qualification and quantification of safety, security and privacy as well as other evaluation criteria using demonstrators. VALU3S will also influence the development of safety, security and privacy standards through an active participation in related standardisation groups. VALU3S will provide guidelines to the testing community including engineers and researchers on how the V&V of automated systems could be improved considering the cost, time and effort of conducting the tests.
VALU3S brings together a consortium with partners from 10 different countries, with a mix of industrial partners (26 partners) from automotive, agriculture, railway, healthcare, aerospace and industrial automation and robotics domains as well as leading research institutes (6 partners) and universities (10 partners) to reach the project goal.
Abstract: The PICK-UP project aims at implementing innovative methods and tools for energy and environmental management and consumption reduction in heterogeneous urban districts. IoT and Fog Computing sensor networks will be integrated with new models of predictive control, energy data analysis and architectures for the aggregation and integration of distributed power generation sources (Microgrids), and flexible demand (Demand Response).
- The issuance of guidelines and tools for the planning and management of sustainable districts (which can then be applied to the concept of smart city).
- The development of an energy management system for sustainable and smart districts
- The creation of a significant smart city demonstration pilot site to be used in local, national and European projects, as well as to attract companies and research institutions at an international level.
Liguria 4P Health (Predictive, Personalized, Preventive, Participatory)
Type POR FESR 2014-2020 Axis 1 “Ricerca ed Innovazione” – Action 1.2.4
Abstract: Development of an innovative solution of personal/mobile healthcare based on the semantic management of clinical data obtained from wearable/environmental created through predictive algorithms in order to create efficient recruiting, care and rehabilitation plans. The system will be delivered in Cloud via App in order to promote the participative interaction between patient and caregiver. A supportive analysis of the Healthcare Services promoters for an appropriate management of the chronicity will be provided.
Results: the three total results of the project are:
- R1: realization of a prototype demonstrating the functionalities of the system/product
- R2: System tests that, if they confirm the positive result concerning the successful achievement of functional goals there will be a Business Plan document attached for the following phase of exploitation. In case of mixed or not totally positive results an analysis of the criticalities will follow the tests.
- R3: study of clinic validation of the system and of PDA/PDTA elaborated during the project in favor of Healthcare Services providers (Enti di Erogazione di Servizi Sanitari)
Safe Cooperating Cyber-Physical Systems using Wireless Communication (SafeCop)
Abstract: SafeCOP addresses operating environments with security constraints such as Cooperating CyberPhysical Systems (CO-CPS) characterised by a prevailing use of wireless communication with multiple stakeholders and open and unpredictable operating environments. In this scenario, no stakeholder has overall responsibility for the resulting "system of systems". Even though CO-CPS allow to face and win different challenges of society (present and future), introduce new applications and markets, their certification and development are not adequately addressed by existing practices. The final goal of SafeCOP was therefore to provide an approach to guarantee the safety (understood both as safety and, where necessary, as security) of CO-CPS, thus enabling their development and certification. In order to reach this goal, the project has defined an architecture based on a run-time manager for the detection of abnormal behaviours that, if necessary, can trigger a "degraded but safe" service mode. SafeCOP has also developed methodologies and tools that can be used to certify the correct and safe functioning of a cooperative system. In addition, SafeCOP has extended current wireless technologies to ensure secure cooperation. Finally, SafeCOP has contributed to make new rules and regulations, providing certification authorities and standardisation committees with the scientific solutions needed to create standards that are also effective in addressing issues related to cooperation in a "system of systems".
- A methodology to ensure the safety of CO-CPS.
- A reference architecture for run-time management to support CO-CPS engineering and certification.
- An extension of current wireless protocols to secure cooperation.
- New standards and regulations.
Abstract: P3C aimed at creating a virtuous circle involving (1) predictive medicine, (2) medical records (Italian Fascicolo Sanitario Elettronico, FSE), (3) diagnostic appropriateness and (4) personalized therapy.
The main goal was to benefit from epidemiological inquiries deriving from big laboratory data integrating them with the knowledge of the FSE in order to spot specific patients and, in that case, report them to doctors through the early referral procedure.
The project involved two other companies: Dedalus (before Noemalife), Italian leader in the Healthcare automation, and Nextage who provided its skills concerning both platforms and interfaces for the management of large data quays and genomic data analysis pipeline.
Partners involved: Dedalus, Nextage
Outcomes: For what concerns Rulex Innovation Labs it has been developed an innovative parallelization component that ensures Rulex's scalability beyond one billion data. Other developments were data breakdown logic patterns, a “map-reduce” algorithm of rules allowing to work in a spread environment (several parallel processing servers and/or data in geographically distant sites).