Classification of stance and swing gait states during treadmill walking from non-invasive scalp electroencephalographic (EEG) signals

In Contreras-Vidal and colleagues have shown the feasibility of inferring the linear and angular kinematics of treadmill walking from scalp EEG. Here, we apply a discrete approach to the same problem of decoding the human gait. By reducing the gait process to a mere succession of Stance and Swing phases for each foot, the average decoding accuracy reached 93.71%. This is sufficient to design a gait descriptor that relies only on this classification of two possible states for each foot over time as input, which could complement the model-based continuous decoding method that lacks in some aspects (foot placement at landing, weight acceptance, etc.). A final implementation of this method could be used in a powered exoskeleton to help impaired people regain walking capability.


Editor(s):
Pons, José L.
Torricelli, Diego
Pajaro, Marta
Published in:
Converging Clinical and Engineering Research on Neurorehabilitation: International Conference on NeuroRehabilitation, 1, 1, 507-511
Presented at:
International Conference on NeuroRehabilitation, Toledo, Spain, November 14-16, 2012
Year:
2012
Publisher:
Springer
ISBN:
978-3642345456
Keywords:
Laboratories:




 Record created 2012-12-17, last modified 2018-03-17

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