DocumentCode :
3685593
Title :
A multi-channel biomimetic neuroprosthesis to support treadmill gait training in stroke patients
Author :
Noelia Chia;Emilia Ambrosini;Walter Baccinelli;Antonio Nardone;Marco Monticone;Giancarlo Ferrigno;Alessandra Pedrocchi;Simona Ferrante
Author_Institution :
Neuroengineering and Medical Robotics Laboratory, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo Da Vinci, 32, 20133, Italy
fYear :
2015
Firstpage :
7159
Lastpage :
7162
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.
Keywords :
"Muscles","Electromyography","Training","Physiology","Sensors","Biomechanics","Integrated circuits"
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE
ISSN :
1094-687X
Electronic_ISBN :
1558-4615
Type :
conf
DOI :
10.1109/EMBC.2015.7320043
Filename :
7320043
Link To Document :
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