More than 50% of people with neurological disorders are fallers and about 14-65% fall at least once while in hospital, due to loss of balance. Neurological diseases limit levels of involvement and produce difficulties in the execution of daily living at home and social and productive activities by compromising the quality of life, as for patients with Parkinson's Disease, post-stroke symptoms, and severe Traumatic Brain Injury. Remarkably, imbalance, reduced mobility, and low daily physical activity may lead to decreased social participation, increased fear of falling, and invoke social isolation.
This project aims to develop a low-cost and easy-to-use technological
device, called FAST (Fall Arrest SysTem), to prevent the risk of
falling able to operate remotely and in real-time with the electronic
medical record of each patient. The gait and postural dynamic stability
assessment is obtained using inertial sensors combined with
electroencephalography (EEG). EEG is a technique commonly used
to study human brain activity.
We perform this clinical evaluation in patients suffering from neurological
disorders, while performing three different motor tasks in an ecological
scenario. Overall, these indices will permit predicting information about
the risk of falling and orienting a tailored therapeutic neurorehabilitation
strategy. FAST will integrate these indexes to facilitate clinicians to i)
classify the risk of falling in real-time in patients with neurological
disorders and ii) have an objective evaluation of the therapeutic outcome daily.
Our project relies on the evidence that current clinical assessment
and rehabilitation practice often lack predictive power/reliability in estimating
patients' independence in the ADL. Our mission is to provide the clinicians with
a new generation of cost-effective prevention tools to tailor function-oriented
neurorehabilitation protocols postural stability and gait disorders.
FAST merges a world-class team of neuroscientists, bioengineers,
and health professionals into a multidisciplinary research team
oriented toward developing a new generation of technological devices.
Our efforts favor the decision-making process in tailoring patient-centered
rehabilitation strategies and improving the health service with direct
benefits for the patients. Our products will make clinical evaluation
better, faster, and more accurate in clinical and natural settings.
As a multidisciplinary team, we can combine brain and body movement
signals to obtain new predictive indices to assess and treat balance
and gait disorders.
Our evidence-informed approach and scientific
method lead us to implement brand-new strategies to optimize the
recovery of cognitive-motor functions. We are confident that integrating
our scientific-based systems with the clinical routine can improve the
management of the health care service and accelerate the achievement
of an objective and reliable telemonitoring and telerehabilitation process.
Also, we firmly believe these strategies should be available for people of
different ages and through all life course stages.
Our success is guaranteed by the tight and synergic collaboration between
the members and the dynamic interplay of scientific and clinical
expertise and previous experience. As a whole, we are representative
of an innovative model of a rehabilitation team in which clinicians and
researchers interact to inspire new perspectives to take care of the
patients.
Viviana Betti
Marco Iosa
Luigi Bianchi
Elena Bergamini
Giuseppe Vannozzi
Marco Tramontano
Valeria Belluscio
Yara El Rassi
Carmine Gelormini
Gabriele Marangon
Amaranta Soledad Orejel Bustos
Simona Vasta
The FAST team is glad to announce a forthcoming scientific event for the dissemination of new technological solutions to assess and treat the postural
Our Research group is one of the winners of Pre-Seed grant with Fall Arrest System (FAST) Project, the aims of the project is develop a low-cost and easy-to-use technological device to prevent the risk of falling able to operate remotely and in real-time with the electronic medical record of each patient (CUP: B85F2100143002).