Rome: March 13th, 2026

Current tag list

From Biolab3

animal failure fluid
fatigue imaging fmri modular
time-space biomechanics motor control
medical sts com synergies
analysis prediction classification dynamics
msd hrc task hd-emg
electromyography accelerometers muscle emg

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

Development of Recurrent Neural Networks for Thermal/Electrical Analysis of Non-Residential Buildings Based on Energy Consumptions Data
E. Belloni, F. Forconi, G. M. Lozito, M. Palermo, M. Quercio, and F. Riganti Fulginei
Energies

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 at developing and offering new approaches, methodological innovations, and technological solutions to be applied in the field of human movement science at large.

The field of application ranges from 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), to the long-term monitoring and description of the quality of human movement and behaviour in unconstrained scenarios, to the development of technologies for human enhancement, rehabilitation, assistance and social inclusion at all age levels.

To this end, EMG, wearable inertial sensors, marker-based and marker-free kinematics, force sensors are used as data sources, and investigated, often in combination. Application fields include performance optimisation in sport activities, risk assessment in ergonomics, motor recovery monitoring in rehabilitation, evaluation of bio-feedback effects on motor control in neuromechanics.



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