GitHub - pavithranp/HumanActivityRecognition: IMU data analysis using deep architectures

Requirements:

Files help:

hyperOpt.py - used to find hyper parameters in the model iteratively, runs for long time .
haptModel.py - Trains a model using the Hapt Dataset
realwolrd.py - Trains a model using the RealWorld Dataset
plot_confusion.py - Creates a visual representation of confusion matrix, required matrix has to be pasted inside the code.
MOD/mod.py - Make your Own Data model for the android application. Creates a tflite model for the android application.
MOD/bucket - Own datasets for the android app model
MOD/movepredict - Android Application folder

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
.ipynb_checkpoints		.ipynb_checkpoints
Dataset/HAPT Data Set		Dataset/HAPT Data Set
Plots/Sensor Data Plots for Different Labels		Plots/Sensor Data Plots for Different Labels
RealWorldData		RealWorldData
.Input_Pipeline.py.swp		.Input_Pipeline.py.swp
Input_pipeline_HAR_Abirami.ipynb		Input_pipeline_HAR_Abirami.ipynb
README.md		README.md
RealWorldmodel250Winsize.hdf5		RealWorldmodel250Winsize.hdf5
haptModel.py		haptModel.py
hyperOpt.py		hyperOpt.py
model_test.py		model_test.py
modelmid.h5		modelmid.h5
plot_confusion.py		plot_confusion.py
realWorld.py		realWorld.py
seq2label.hdf5		seq2label.hdf5
seq2seqmodel.hdf5		seq2seqmodel.hdf5

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