Rome: October 29th, 2026

Current tag list

From Biolab3

gait fatigue ultrasound failure
postural prediction prosthetics
sway functionality analysis msd
dynamics time-space task kinematics
skeletal hd-emg emg fluid
hrc muscle

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

Mapping forearm muscle activity with a single linear HD-EMG grid and muscle synergy model during hand-wrist movements
F. Forconi, I. De Meis, A. De Nobile, M. Schmid, S. Conforto, and S. Ranaldi
47th Annual International Conference of IEEE Engineering in Medicine and Biology Society (EMBS 2025)

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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