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Deep Learning to identify gait events

Project description

DeepEvent


We propose a new application (DeepEvent) of long short term memory recurrent neural network to the automatic detection of gait events. The 3D position and velocity of the markers on the heel, toe and lateral malleolus were used by the network to determine Foot Strike (FS) and Foot Off (FO). The method was developed from 10526 FS and 9375 FO from 226 children. DeepEvent predicted FS within 5.5 ms and FO within 10.7 ms of the gold standard (automatic determination using force platform data) and was more accurate than common heuristic marker trajectory-based methods proposed in the literature and another deep learning method.


Requirement

Linux, Python 2.7, Keras, Tensorflow, Btk, Numpy, Scipy.


Installation

pip install deepevent


Running DeepEvent

deepevent FilenameIn.c3d FilenameOut.c3d where FilenameIn.c3d is the c3d file to identify gait events, FilenameOut.c3d is the new file with the gait events.


Next step

Python 3.7, Windows


Contact

mathieu.lempereur@univ-brest.fr Mathieu LEMPEREUR CHRU de Brest - Hopital Morvan Service MPR 2 avenue Foch 29609 BREST cedex FRANCE

Project details


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Files for deepevent, version 0.2
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