PRIN2010-2011

A quantitative multifactorial approach for the estimate and prevention of fall risk in elderly

BioRobotics Institute Role: 
Partner

Principal Investigator: 
Angelo Maria Sabatini


Project Lifetime
Feb 2013 to Jan 2016

Funding Institutions: 

Italian Ministry of Education, University and Research, MIUR under the PRIN programme 2011

Research Program: 
PRIN area 09 - Industrial and Information Engineering
Contribution to SSSA: 
106.863
 
Summary: 

A healthy and independent life is a major challenge for the growing old European people. Millions of Europeans are afflicted by mobility impairments, ranging from accidental falls to difficulty standing up and/or walking. In particular, elderly are further prone to fall due to visual deficit and lackness of equilibrium. In fact, a third of over 65 European citizens living in senior centers fall once per year, with bad consequences of physical, psychological and economic nature. Falls often imply serious injuries, such as fractures (hip, femur etc...) or contusions, resulting, in turn, in a severe worsening of quality of life and loss of independence. In addition, several post-fall disorders, which led to anxiety and intense fear are documented in literature.
Presently, falls are handled with a reactive model: fall risk is evaluated by physicians and physiotherapists only after the traumatic event. In the most favorable scenario, wearable sensors and alarm devices promptly notify the emergency to an tele-assistance centre which is able to provide help to fallers.
However, a proactive model, involving focused prevention campaigns, could avoid most of falls and the resulting long term treatments. The outcomes of such strategies would be the improvements of elderly's quality of life and a huge saving of economic resources.
The aims of this project is to provide quantitative tools to:
1) predict fall risk;
2) predict post fall fracture risk;
3) prevent falls in elder people;
The principal factors which induce falls happening will be extracted from a retrospective dataset, by using a modelistic approach: falls will be caracterized and classified trough experimental protocols and algorithms to indentify its dynamical features.
In this way, critical subjects could be identified and selected for a customized rehabilitation procedure.
In addition, different rehabilitation approaches will be implemented, aiming at decreasing fall risk:
1) training EMG-audio feedback;
2) walking and equilibrium training with neuro-muscular electrical stimulation;
3) walking training in virtual reality environment.

All treatments and procedures will be validated trough experimental studies in different clinical centres.
Such methodologies, whether they become commercially available, would allow risk estimation, improving clinical practice.


Partners: 

Università degli Studi di BOLOGNA
Università degli Studi di ROMA "Foro Italico"
Università degli Studi del PIEMONTE ORIENTALE "Amedeo Avogadro"-Vercelli
Università degli Studi di PAVIA
Università degli Studi di SASSARI
Politecnico di TORINO
Politecnico di MILANO

 

 
 

Meet the team