Brainbox proposes an integrated multimodal sensing and AI approach for an automated, computer controlled preclinical system, referred to as “smart box”. Specifically, the “smart box” will enable to concomitantly monitor body-level behaviour and biomechanics (kinematics and dynamics, joints torque estimations, etc.), physiological biosignals (heartbeat and respiration) and spontaneous/evoked neural activity (single cells and populations activity) across various brain circuits, and advance current capabilities to quantify motor and non-motor functions of behaving mice disease models. The research plan targets the realization of a behavioural monitoring system by integrating piezoelectric wearable sensors, chronically implantable active dense bi-directional (recording/stimulation) CMOS-based electrode array probes, and sensors (capacitive sensing floors and video cameras) for the use of novel biomechanical computational mice models and AI tools. To validate the acquisition of multiscale experimental data, we will conduct experimental trials using the proposed smart box in a Parkinson’s disease (PD) mouse model. This process will involve a multiparametric analysis of the acquired multiscale data, which will be used to assess the system’s sensitivity in quantifying both motor and non-motor symptoms exhibited by the PD mice model. Feasibility of Brainbox is ensured by the already developed sensing and AI technologies in the Brainbox consortium, as well as by an interdisciplinary team with recognized engineering and neuroscience expertise. The proposed preclinical smart box system and multimodal sensing approach for multiscale data monitoring will enhance understanding of disease models and treatment effects with unprecedented mechanistic depth, thus advancing current research capabilities to identify biomarkers of disease progression and potential therapeutic strategies.
We have a joint laboratory with Honda Research Institute Japan (HRI-JP) whose main aim is to develop advanced control, perception, and planning algorithms for human-robot collaboration scenarios.
The joint lab keywords, which characterise the research we develop, are ergonomy, cobots, and human-robot collaboration.
Besides iCub, the conceived algorithms for human-robot collaboration will be tested on the ED-2R and Asimo humanoid robots.
Danieli Automation’s joint lab focuses on the development of flexible robotic systems and automated solutions in the field of steel processing.
Our aim is to increase safety for workers in hazardous industrial environments by using advanced control, perception and planning algorithms.
The Camozzi Group is a market leader in the production of components for the pneumatics automation with applications in the life science, health and the textile industry.
As a research line, we are applying our expertise in the modeling, control, and planning of nonlinear systems to the pneumatic field.
The goal of the project is to devise future wearable technologies and humanoid robots to maximise work ergonomy and technological acceptability of future industry and healthcare environments.
INAIL is the Italian National Institute for Insurance against Accidents at Work, it is a public authority that manages compulsory insurance against accidents at work and occupational diseases.
The collaboration aims to adapt the iCub Robot’s Avatar architecture to develop a commercial, human-controlled wheeled Avatar Robot in a simulation environment. We will establish the Avatar simulation infrastructure to ensure seamless component operation. The project will integrate advanced manipulation, voice, and visual interfaces for better human-robot interaction, and implement tactile feedback and locomotion controls for precise and realistic movements.
The spin-off idea from AMI is aiming at providing an integrated platform that enables real-time human health monitoring in terms of motion tracking, articular stress monitoring, and fatigue analysis. Based on our extensive and in-depth research analysis, we developed groundbreaking technology.
iFeel will be the next step for life-logging human health monitoring for industrial, rehabilitation, sports and gaming applications.
Completed projects
Marie Sklodowska-Curie Innovative Training Networks under Grant Agreement No. 642667
Role: supervision of a PhD student funded by the project
Research and Innovation Programme under Grant Agreement No. 731540
Role: coordination - Francesco Nori - and PI - Daniele Pucci.
FP7