CARE - Companion for Aging with Resilience and Evaluation 

Leading Industry: Protom Robotics

Academic Scientific Coordinator: Mariacarla Staffa

CARE is a research project funded by the Italian Ministry of University and Research—MIUR, within the BAC programme: AGE-IT SPOKE 3 – CUP F33C22000490006 (Codice Progetto AGE-IT PE00000015) 

The CARE project aims to develop an assistive companion robot that monitors and assesses the health of elderly individuals with multimorbidity and frailty. Its goal is to create a model that allows the robot to dynamically detect health changes and activate appropriate technologies for personalized, non-invasive support.

CARE will integrate Natural Language Processing (NLP), Large Language Models (LLM), and wearable/non-wearable sensors to gather data and interact naturally with users, addressing challenges in usability, quality of life, and privacy concerns

SymAIbot - Companion for Aging with Resilience and Evaluation 

Leading industry: Martec

Partner: Logogramma

Scientific Advisor: Mariacarla Staffa

SymbAIbot is a research project funded by the Italian Ministry of University and Research—MIUR, within the BAC programme: “FUTURE ARTIFICIAL INTELLIGENCE – FAIR” Codice Progetto PE00000013 CUP H97G22000210007 - Spoke 6: SymbioticAI

The project aims to develop a symbiotic interaction pattern between robots and humans using artificial intelligence in industrial automation (SAIRIS – Symbiotic AI for Robotics in Industrial Settings). It also seeks to create an experimental prototype (TRL 6), SymAIBot, as a real-world implementation of the defined pattern. SymAIBot functions as a system of expert agents managing a diverse fleet of humanoid, animal-like cobots, and autonomous rovers. Each SymAIBot agent establishes a case-specific symbiotic operator-robot relationship based on the application context.

AIDA - Artificial Intelligence Design Assistant

Industry Leading Partner: Aieng srl

Scientific Consultant: Mariacarla Staffa

AIDA is a research project funded by Meditech n.3 - PNRR) – MISSIONE 4 COMPONENTE 2

“Dalla ricerca all’impresa” INVESTIMENTO 2.3 “Potenziamento ed estensione tematica e territoriale dei centri di trasferimento tecnologico per segmenti di industria”

The AIDA project, “Artificial Intelligence Design Assistant – Connecting the Dots of the Engineering and Production Digital Thread,” aims to drive a radical shift in the design process, with a significant impact on production efficiency and quality, creating a competitive advantage for manufacturing and aerospace companies. In recent years, both the industrial and aerospace sectors have been undergoing a technological transition from mechanical to digital systems, as well as a transformation in business models, shifting from a product-centric approach to servitization.

RESTART - Robot Enhanced Social abilities based on Theory of mind for Acceptance of Robot in assistive Treatments

Scientific Coordinator: Mariacarla Staffa

RESTART is a research project funded by the Italian Ministry of University and Research—MIUR, within the PRIN2022

The RESTART project aims at designing and developing a new human-robot interactive paradigm, in which the robot is humanized thanks to empowered AI-based social abilities based on the concept of theory of mind and mutual-understanding in order to improve the acceptability of robots especially in assistance scenarios, where higher legibility and conformity to cognitive and emotional aspects of the interaction are required.

SPECTRA - Supporting schizophrenia PatiEnts’ Care wiTh Robotics and Artificial Intelligence

Scientific Coordinators: Rita Francese and Mariacarla Staffa

SPECTRA is a research project funded by the Italian Ministry of University and Research—MIUR, within the PRIN-PNRR-2022

The SPECTRA Project aims at designing a Decision Support System (DSS) based on AI techniques and the use of cutting-edge Technologies for the early diagnosis of TRS patients. The innovative aspect of the Project is to combine typical screening procedures used in standard clinical practice, with ICT-based assessment techniques based on Machine Learning algorithms.

UBRECT - Undersampled BREst Computed Tomography

Scientific Coordinator: Mariacarla Staffa and Fabio Baselice

UBRECT is a research project funded by the Italian Ministry of University and Research—MIUR, within the PRIN-PNRR-2022

UBRECT porject aims to provide a considerable improvement to the Breast Computed Tomography clinical exam by the development of specific algorithms from BCT image formation that can exploit much lower data providing a reconstructed image with a quality level similar to before.The expected advantage is two-fold: i) BCT images with fewer projections will be beneficial for the woman under investigation both in terms of safety and comfort, as the X-ray radiation dose will be sensibly lowered and the acquisition time will also be expected to decrease; ii) On a clinic side, the reduction in terms of acquired projects could be exploited for multi-energy acquisitions, achieving much more informative data for the pathologies characterization

Artificial Intelligence in Medicine, AIM was a research project funded by INFN–CSN5 in 2019-2021. The next_AIM project is currently funded by INFN-CSN5 for the tree-year period 2022-2024

The Artificial Intelligence in Medicine (AIM) project aims to exploit the expertise of INFN and associated researchers on medical data processing and enhancement, and turn it in the development of advanced and effective analysis instruments to be eventually clinically validated and translated into products.

https://www.pi.infn.it/aim/

This research aims to explore the digital transformation processes, and the related changes, that are occurring in the knowledge-intensive sectors such as Universities. Specifically, the project - following the “case study” (Yin, 2003) approach - aims to examine how the University of Naples Parthenope, by exploiting the opportunities offered by emerging digital technologies, can successfully embrace more innovative, sustainable, and inclusive organizational models, strategic paths of growth, and working practices/processes for successful advantage.