Matteo Bertucco

Prof. Matteo Bertucco

University of Verona
orcid

Research Focus & Metrics

Academic Career Length

9Years (2018-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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13 July 2022 in Computer Methods and Programs in Biomedicine
Verified
Journal Article
Closed access
Enrico MartiniEnrico Martini
Enrico Martini
Michele BoldoMichele Boldo
Michele Boldo
SAStefano Aldegheri
Stefano Aldegheri
Nicola ValèNicola Valè
Nicola Valè
Mirko FilippettiMirko Filippetti
Mirko Filippetti
NBNicola Bombieri
Nicola Bombieri

8 August 2022 in Neuroscience
Verified
Journal Article
Closed access
MNMauro Nardon
Mauro Nardon
FPFrancesco Pascucci
Francesco Pascucci
PCPaola Cesari
Paola Cesari
MBMatteo Bertucco
Matteo Bertucco
MLMark L. Latash
Mark L. Latash

6 February 2024 in Computers in Biology and Medicine
Verified
Journal Article
Open access
Michele BoldoMichele Boldo
Michele Boldo
Roberto Di MarcoRoberto Di Marco
Roberto Di Marco
Enrico MartiniEnrico Martini
Enrico Martini
MNMauro Nardon
Mauro Nardon
MBMatteo Bertucco
Matteo Bertucco
NBNicola Bombieri
Nicola Bombieri
Motion analysis is crucial for effective and timely rehabilitative interventions on people with motor disorders. Conventional marker-based (MB) gait analysis is highly time-consuming and calls for expensive equipment, dedicated facilities and personnel. Markerless (ML) systems may pave the way to less demanding gait monitoring, also in unsupervised environments (i.e., in telemedicine). However,scepticism on clinical usability of relevant outcome measures has hampered its use. ML is normally used to analyse treadmill walking, which is significantly different from the more physiological overground walking. This study aims to provide end-users with instructions on using a single-camera markerless system to obtain reliable motion data from overground walking, while clinicians will be instructed on the reliability of obtained quantities. The study compares kinematics obtained from ML systems to those concurrently obtained from marker-based systems, considering different stride counts and subject positioning within the capture volume. The findings suggest that five straight walking trials are sufficient for collecting reliable kinematics with ML systems. Precision on joint kinematics decreased at the boundary of the capture volume. Excellent correlation was found between ML and MB systems for hip and knee angles (0.92<R2<0.96), with slightly lower correlations observed for ankle plantar-dorsiflexion. The Bland-Altman analysis indicated the largest bias for hip flexion/extension ([0.2∘,10.9∘]) and the smallest for knee joint ([0.1∘,0.8∘]) when comparing MB-PiG and MB-JC approaches. For MB-JC vs. ML-JC comparison, the largest bias was for the ankle joint ([1.2∘,11.8∘]), while the smallest was for the hip joint ([0.2∘,7.3∘]). Single-camera markerless motion capture systems have great potential in assessing human joint kinematics during overground walking. Clinicians can confidently rely on estimated joint kinematics while walking, enabling personalized interventions and improving accessibility to remote evaluation and rehabilitation services, as long as: (i) the camera is positioned to capture someone walking back and forth at least five times with good visibility of the entire body silhouette; (ii) the walking path is at least 2 m long; and (iii) images captured at the boundaries of the camera image plane should be discarded.

8 March 2022 in Neuroscience
Verified
Journal Article
Closed access
PCPaola Cesari
Paola Cesari
FPFrancesco Piscitelli
Francesco Piscitelli
FPFrancesco Pascucci
Francesco Pascucci
MBMatteo Bertucco
Matteo Bertucco

1 October 2021 in Gait & Posture
Verified
Journal Article
Closed access
MNMauro Nardon
Mauro Nardon
FRFederico Ruzzante
Federico Ruzzante
LOLeslie O’dOnnell
Leslie O’dOnnell
AAAlessandra Adami
Alessandra Adami
SDSudarshan Dayanidhi
Sudarshan Dayanidhi
MBMatteo Bertucco
Matteo Bertucco

1 August 2020 in International Journal of Human-Computer Studies
Verified
Journal Article
Closed access
CBCassie N. Borish
Cassie N. Borish
MBMatteo Bertucco
Matteo Bertucco
TSTerence D. Sanger
Terence D. Sanger