Our Healthcare research team applies advanced machine learning and microfabrication technologies to the design of innovative products and solutions for the Healthcare industry.
We work in partnership with internationally recognized research centres, such the Institute of Neuroscience and IMEM (Institute of Materials for Electronics and Magnetism) of the INRC (Italian National Research Council), with whom we operate joint research laboratories.
Our team consists of experts in biomedical engineering, machine learning, computer science, electronics, physics, material science and telecommunications, complemented by CNR’s neuroscientists and material scientists. We also collaborate with global industrial, academic and clinical partners across several projects. Current projects include: Brain Computer Interfaces, Decision Support Systems for surgical epilepsy treatment and neurorehabilitation. Following successful outcomes from these projects, we are conducting new research in the area of predictive medicine applied to Alzheimer disease.
Brain Computer Interfaces
Brain Computer Interface (BCI) is a communication technology that maps brain cognitive or sensory-motor functions into machine readable commands, and has significant applications, not only in the biomedical field.
In partnership with CNR’s Institute of Neuroscience and leveraging on our machine learning platform for pattern recognition and on the latest neuroscientific findings, we are developing motor-based, opportunistic, EEG-based BCIs, applied to, for example, real-time control of robotic devices and advanced driver-vehicle interaction.
Nervous system injuries (caused by, for example, stroke) often cause physical impairments, and current rehabilitation methods have been recognized to be suboptimal from many aspects. We are developing wearable and portable tools to improve the rehabilitation process in an effective but non-invasive way. We develop machine learning based systems treating signals from wireless networked inertial sensors, that can not only learn, model and guide the patient in completing physical exercises, but also support them in their activities of daily leaving.
We have demonstrated a prototype system able to learn and model a yoga exercise in a completely data-driven, unsupervised way, and analyse the quality of the gesture in healthy subjects. We are currently undertaking research to apply this technology to rehabilitation exercises, and also to develop tools to support the patient with immediate context-aware feedback while performing activities of daily living in their own home.
A fraction of pharmacologically intractable epileptic patients require treatment by resective surgery. A positive outcome of the procedure and the absence of adverse effects strongly depend on the correct and specific identification of the epileptic network, i.e. of the area(s) of the brain that are involved in the generation and propagation of the seizure.
In collaboration with CNR’s Institute of Neuroscience we are developing a tool to support the clinician in the identification of the epileptic network, based on the automatic identification, analysis and statistical characterization of interictal epileptiform discharges in long intracranial recordings.
Advanced Fabrication Technologies
In partnership with the IMEM-CNR (Bioelectronics group of the Institute of Materials for Electronics and Magnetism of the Italian National Research Council) we have opened a joint laboratory where we carry out basic and applied research focused towards wearable applications for health monitoring and diagnostics, based on organic devices made of conductive polymers (which have the unique ability to conduct ions in addition to electrons). Organic and flexible transistors with high transconductance are an ideal bridge between biology and electronics. Our miniaturized devices are manufactured through an advanced Jet Printing Technology allowing high control on materials deposition over the microscale.
We are developing a fully mobile system to monitor athletes performance and physically injured patients based on these new wearable sensors. This research, undertaken also in collaboration with leading sports performance and injury partners, has been partially funded by the Regione Emilia Romagna under the Therseo project.