Rome: September 11th, 2026

Current tag list

From Biolab3

failure surface task time-space
sway kinematics prediction gait
hd-emg motor fluid postural
analysis muscle skeletal control
prosthetics classification emg
functionality electromyography dynamics hrc
fatigue

Present results and methods of vectorcardiographic diagnostics of ischemic heart disease
J. Kijonka, P. Vavra, M. Penhaker, D. Bibbo, P. Kudrna, J. Kubicek
Computers in Biology and Medicine

Cognitive control network and language reorganization in patients with brain tumors
L. Pasquini, A. Napolitano, L. Spitoni, M. Schmid, F. Dellepiane, M. Jenabi, K. Peck, A. Holodny
American Journal of Neuroradiology

Anticipatory reaching motor behavior characterizes patients within the Alzheimer’s disease continuum in a virtual reality environment
A. de Nobile, I. Borghi, P. De Pasquale, D.J. Berger, A. Maselli, F. Di Lorenzo, E. Savastano, M. Assogna, A. Casarotto, D. Bibbo, S. Conforto, F. Lacquaniti, G. Koch, A. d’Avella, M. Russo
Alzheimer's Research & Therapy

BioLab3

Biomedical Engineering Laboratory

Phone Number +39 06 5733 7057
Website http://biolab.uniroma3.it
Founder Tommaso D'Alessio
Research group head Silvia Conforto
Lab coordinator Maurizio Schmid
to send an email please replace AT with @

BioLab³, the Biomedical Engineering Laboratory at the Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, aims to develop and promote novel approaches, methodological innovations, and technological solutions for applications in human movement science at large.

The lab operates across a broad range of applications, including the functional evaluation and analysis of motor and physiological markers associated with neuromuscular disorders and conditions (e.g. Parkinson's disease, stroke, prosthesis use, ageing), the long-term monitoring and characterisatin of human movement and behaviour in unconstrained environments, and the development of technologies for human enhancement, rehabilitation, assistance and social inclusion across all age groups.

To this end, data are collected using electromyography (EMG), wearable inertial sensors, marker-based and marker-free motion capture systems, and force sensors, often in integrated cofngiurations. Application domains include performance optimisation in sport, ergonomicrisk assessment, monitoring motor recovery in rehabilitation, and the evaluation of biofeedback effects on motor control within neuromechanics.



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