Evaluation of leg joint trajectories while carrying out passive manipulation by NEUROBike.

M. Coscia, G. Galardi, V. Monaco, S. Bagnato, S. Micera

Research output: Contribution to journalArticlepeer-review


During the last decades, many robotic platforms aimed at post-stroke neurorehabilitation of locomotion have been developed. These devices have been designed to enhance the possibilities of conventional rehabilitation providing safe, highly accurate, intensive and prolonged treatments. Nevertheless, up to now, robotic aided therapy has not yet promoted improvements of the motor performance significantly greater than those achieved by the conventional therapy. According to previous studies, we believe that this result may be partially ascribed to two main issues: the rehabilitation mediated by robots is usually provided too late from the trauma and it mainly consists of passive and cyclic manipulation of the legs. Our proposal to overcome some of the supposed limits is NEUROBike, an operative mechatronic platform able to lead leg manipulation as soon as possible after the trauma, that is when patients still lie on their own beds. Moreover, NEUROBike has been designed to provide both passive and cyclic manipulation of leg joints with trajectories similar to those related to natural walking, and motor task involving random efforts. This work presents the comparison between desired and measured leg joint trajectories while NEUROBike provides cyclic and passive leg manipulation. The results show that angular excursions at proximal joints were reasonably comparable with those obtained by the velocity based model even though they were affected by a positive offset involving emphasized flexion of hip and knee during the gait cycle.

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics


Dive into the research topics of 'Evaluation of leg joint trajectories while carrying out passive manipulation by NEUROBike.'. Together they form a unique fingerprint.

Cite this