Project details
Title:
Sensing and AI Techniques for Aging Well
Acronym:
AGE-SENSEAI
Type:
National (Total Funding Amount: 255.000,00 €) – Progetto a cascata a valere sulle risorse del Piano Nazionale per la Ripresa e Resilienza (PNRR), la creazione e il rafforzamento di “Partenariati estesi alle università, ai centri di ricerca, alle aziende per il finanziamento di progetti di ricerca di base”, finanziato dall’Unione Europea – NextgenerationEU, progetto “AGE-IT – AGEING WELL IN AN AGEING SOCIETY” – Tematica s0902 – Data fusion techniques integrated in measurement procedures to optimize the sensor network and complex features extraction
Start Date:
September 1, 2024
End Date:
September 30, 2025
Principal Investigator:
DII Unit Responsible – Andrea Monteriù
Other Units Involved:
UNIVPM (Lead Partner with DII -DIISM) – UNIVERSITÀ DI MESSINA – P.M.F. SRL
Keywords:
Active Aging; Sensor Networks; Artificial Intelligence (AI); Multi-Resident Monitoring; Data Fusion (DF)
Description:
AGE-SENSEAI aims to develop an innovative sensing ecosystem to support the well-being of elderly people living in multi-resident home environments. The project focuses on personalized assistance and the measurement of comfort and daily activities through an integrated network of non-invasive sensors, wearable devices, and mobile robots. By leveraging Data Fusion (DF) and Artificial Intelligence (AI) techniques, the system will accurately monitor behaviors, detect potential risks such as falls, and provide tailored information to caregivers. AGE-SENSEAI addresses the complex challenge of multi-user environments, often neglected due to cost and measurement difficulties, by optimizing sensor networks and reducing hardware expenses. Co-design sessions with elderly users and caregivers will guide the system’s development to ensure usability and social acceptance. The solution will be validated in a Living Lab with real users, paving the way for practical and scalable deployment.
Objectives:
The AGE-SENSEAI project aims to develop an innovative sensing ecosystem for monitoring the activities and comfort of elderly people living in multi-resident home environments. The main objectives are: (1) to design a non-invasive sensor network integrated into the environment, wearable devices, and mobile robots to track behavioral changes, daily activities, and potential risks such as falls; (2) to apply Data Fusion (DF) techniques to reduce data uncertainty and enhance accuracy; (3) to use Artificial Intelligence (AI) and Explainable AI (XAI) methods to identify individual activities and comfort levels for each resident and optimize the sensing network; (4) to create user-friendly interfaces for caregivers to access relevant data and services; (5) to validate the proposed solution in a Living Lab with real users, ensuring technological and social acceptance; (6) to explore the solution’s scalability and replicability for broader social and industrial applications through user feedback and commercial assessment.
Application Contexts:
AGE-SENSEAI is designed for home environments where elderly people live together in multi-resident settings, such as shared apartments, assisted living facilities, or family homes with multiple elderly occupants. These complex environments require advanced sensing solutions to accurately monitor individual activities and comfort levels. The project addresses the need for non-invasive, cost-effective, and personalized monitoring systems that can operate reliably in shared spaces. Application contexts include the prevention of health risks such as falls, the continuous tracking of daily activities, and the improvement of environmental comfort for each resident. The system also supports caregivers by providing meaningful, user-specific information through dedicated interfaces. AGE-SENSEAI is particularly suitable for active aging programs, smart home solutions, Ambient Assisted Living (AAL) applications, and community-based elderly care services, aiming to enhance quality of life and promote independent living.
Expected Results:
AGE-SENSEAI will deliver an advanced sensing ecosystem capable of accurately monitoring the activities and comfort of elderly individuals in multi-resident home environments. The project will achieve the integration of non-invasive sensors, wearable devices, and mobile robots into a unified system supported by Data Fusion (DF) and Artificial Intelligence (AI) techniques. The expected results include: reduced data uncertainty, improved accuracy in identifying individual activities and comfort levels, and a reduction in hardware costs compared to typical smart home solutions. The project will also develop user-friendly interfaces for caregivers, enabling personalized support and timely interventions. Validation activities in a Living Lab will involve 20 real users to ensure practical effectiveness and social acceptance. Additionally, the project aims to demonstrate the system’s scalability and potential for market adoption, with a target user acceptance rate of at least 70-80%.
Achivied Results:
The project is still ongoing
Project Web Site:
ageit.eu/wp/
Project Social Accounts:
LinkedIn (linkedin.com/company/italian-ageing-age-it-s-c-a-r-l/)
X (twitter.com/Pe8Age_It)
YouTube (youtube.com/@PE8_Age-it/featured)
Web Site of the DII Laboratory:
Laboratory of Artificial Intelligence Robotics (LAIR)
Project Image:
Project Logo: