Rome: August 4th, 2020

Current tag list

From Biolab3

ergonomics dmd dual cognitive
electrical kinematics index signals
muscle social neurorehabilitation emg
pedals task cycling coactivation
synergies processing stimulation eeg
myoelectric inertial reality engagement
analysis stroke posture
fem functional sensors walking
virtual ann signal

Computer Aided Effective Prediction of Complete Responders After Radiochemotherapy Based on Tumor Regression Grade Estimated by MR Imaging
C. Losquadro, S. Conforto, M. Schmid, G. Giunta, M. Rengo, D. Caruso, A. Laghi
VII ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing

Common and specific gait patterns in people with varying anatomical levels of lower limb amputation and different prosthetic components
T. Varrecchia, M. Serrao, M. Rinaldi, A. Ranavolo, S. Conforto, C. De Marchis, A. Simonetti, I. Poni, S. Castellano, A. Silvetti, A. Tatarelli, L. Fiori, C. Conte, F. Draicchio
Human Movement Science

Optimizing the Scale of a Wavelet-Based Method for the Detection of Gait Events from a Waist-Mounted Accelerometer under Different Walking Speeds
C. Caramia, C. De Marchis, M. Schmid
Sensors

BioLab3

Biomedical Engineering Laboratory

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