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        "description": "# DeepEvent\n---\nWe propose a new application (_DeepEvent_) of long short term memory recurrent neural network to the automatic detection of gait events.\nThe 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). \nThe 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.\n\n---\n## Requirement\nLinux, Python 2.7, Keras, Tensorflow, Btk, Numpy, Scipy.\n\n---\n## Installation\n`pip install deepevent`\n\n---\n## Running _DeepEvent_\n`deepevent FilenameIn.c3d FilenameOut.c3d`\nwhere FilenameIn.c3d is the c3d file to identify gait events, FilenameOut.c3d is the new file with the gait events.\n\n---\n## Next step\nPython 3.7, Windows\n\n---\n## Contact\n[mathieu.lempereur@univ-brest.fr](mailto:mathieu.lemepreur@univ-brest.fr)\nMathieu LEMPEREUR\nCHRU de Brest - Hopital Morvan\nService MPR\n2 avenue Foch\n29609 BREST cedex\nFRANCE",
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