Rome: September 23rd, 2020

Current tag list

From Biolab3

wavelet analysis cycling signal
inertial processing posture asymmetry
amputees electrical parameters muscle
functional synergies accelerometer stimulation
sensors task amputation cognitive
force dual emg stroke
algorithms load dmd
walking neurorehabilitation gait coactivation

‘Prosocial’ virtual reality as tool for monitoring engagement in intergroup helping situations
F. D'Errico, M. Schmid, M. Martinez, S. Mastrobattista, R. Parlongo, C. Massom, C. D'Anna
Proceedings of the Workshop Socio-Affective Technologies: an interdisciplinary approach co-located with IEEE SMC 2019 (Systems, Man and Cybernetics)

A novel technique to design and optimize performances of custom load cells for sport gesture analysis
D.Bibbo, S.Gabriele, A. Scorza, M. Schmid, S.A. Sciuto, S. Conforto

Automated segmentation of colorectal tumor in 3D MRI sing 3D multiscale densely connected convolutional neural network
MH. Soomro, M. Coppotelli, S. Conforto, M. Schmid, G. Giunta, L. Del Secco, E. Neri, D. Caruso, M. Rengo, A. Laghi
Journal of healthcare engineering


Biomedical Engineering Laboratory

Phone Number +39 06 5733 7057
Fax +39 06 5733 7026
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 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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