Abstract
This study presents an innovative multi-channel neuroprosthesis that induces a biomimetic activation of the main lower-limb muscles during treadmill gait training to be used in the rehabilitation of stroke patients. The electrostimulation strategy replicates the physiological muscle synergies used by healthy subjects to walk on a treadmill at their self-selected speed. This strategy is mapped to the current gait sub-phases, which are identified in real time by a custom algorithm. This algorithm divides the gait cycle into six sub-phases, based on two inertial sensors placed laterally on the shanks. Therefore, the pre-defined stimulation profiles are expanded or stretched based on the actual gait pattern of each single subject. A preliminary experimental protocol, involving 10 healthy volunteers, was carried out to extract the muscle synergies and validate the gait-detection algorithm, which were afterwards used in the development of the neuroprosthesis. The feasibility of the neuroprosthesis was tested on one healthy subject who simulated different gait patterns, and a chronic stroke patient. The results showed the correct functioning of the system. A pilot study of the neurorehabilitation treatment for stroke patients is currently being carried out.
Original language | English |
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Title of host publication | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 7159-7162 |
Number of pages | 4 |
Volume | 2015-November |
ISBN (Print) | 9781424492718 |
DOIs | |
Publication status | Published - Nov 4 2015 |
Event | 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy Duration: Aug 25 2015 → Aug 29 2015 |
Other
Other | 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 |
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Country | Italy |
City | Milan |
Period | 8/25/15 → 8/29/15 |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Signal Processing
- Biomedical Engineering
- Health Informatics