Authors:
Nicoletta Balletti
1
;
2
;
Antonella Cascitelli
3
;
Patrizia Gabrieli
3
;
Emanuela Guglielmi
2
;
Gennaro Laudato
2
;
Aldo Lazich
1
;
Marco Notarantonio
1
;
Rocco Oliveto
4
;
2
;
Stefano Ricciardi
2
;
Simone Scalabrino
2
and
Jonathan Simeone
4
Affiliations:
1
Center for Biotechnology, Institute of Biomedical Sciences of the Ministry of Defense, Rome, Italy
;
2
University of Molise, Pesche (IS), Italy
;
3
Atlantica Digital spa, Rome, Italy
;
4
Datasound srl, Pesche (IS), Italy
Keyword(s):
Virtual Assistant, Home Rehabilitation, Artificial Intelligence, Motion Capturing.
Abstract:
Mobility impairments reduce the ability of patients to complete daily activities. Physio-therapeutic exercises help patients address such limitations. Correctly executing these exercises is crucial, often requiring a physiotherapist’s guidance. To address this need, combining advanced sensors with artificial intelligence offers a promising solution for home rehabilitation, enabling remote monitoring and reducing stress. In this paper, we introduce VIRTUAL-PHYSIO, a virtual assistant for remote rehabilitation integrated into a home-deployable low-cost physiotherapy monitoring system 2VITA-B PHYSICAL. VIRTUAL-PHYSIO provides real-time feedback during rehabilitation exercises and evaluates entire sessions, allowing physiotherapists to focus on critical cases. We experimented with VIRTUAL-PHYSIO on 51 individuals whose performances were also evaluated by a physiotherapist as a reference. The results (i) highlight good patient acceptability for the virtual assistant, and (ii) show that th
e proposed machine learning approach can effectively perform an automated evaluation of rehabilitative movements.
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