Rome: August 30th, 2026

Current tag list

From Biolab3

onset upper hd-emg reaching
control cop task prediction
hrc muscle fluid vr
failure fatigue detection analysis
ultrasound postural limb sway
ergonomics dynamics motor
time-space electromyography emg

Automatic handwriting recognition with a minimal EMG electrodes setup: a preliminary investigation
A. Tigrini, S. Ranaldi, A. Mengarelli, F. Verdini, M. Scattolini, R. Mobarak, S. Fioretti, S. Conforto, L. Burattini
2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

Image quality assurance for B-mode diagnostic ultrasound: Kiviat-based protocol first application
G. Fiori, M. Schmid, J. Galo, S. Conforto, S.A. Sciuto, A. Scorza
2024 IEEE International Workshop on Metrology for Industry 4.0 & IoT

Intelligent human–computer interaction: combined wrist and forearm myoelectric signals for handwriting recognition
A. Tigrini, S. Ranaldi, F. Verdini, R. Mobarak, M. Scattolini, S. Conforto, M. Schmid, L. Burattini, E. Gambi, S. Fioretti, A. Mengarelli
Bioengineering

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 @

BioLabÂł, 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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