GitHub - harrywaugh/ActivityTrackingWithSensorFusion: I wrote my dissertation for my Computer Science degree on whether the accuracy of GPS tracking for sports activities (running, cycling, walking) could be improved using additional sensors found in a smartphone. Initially, I implemented a Kalman Filter to fuse the readings of each sensor, this was written in python and used popular libraries like NumPy. I then researched and taught myself the theory behind Recurrent Neural Networks (RNNs) in order to implement them using tensorflow and keras. This project was supported by the Oracle Innovation Accelerator program, who generously gave me access to a cloud compute instance.

Name		Name	Last commit message	Last commit date
.ipynb_checkpoints		.ipynb_checkpoints
Data		Data
__pycache__		__pycache__
archive		archive
.bashr		.bashr
.gitattributes		.gitattributes
.gitignore		.gitignore
Butterworth Low Pass Filter.ipynb		Butterworth Low Pass Filter.ipynb
DataStream.py		DataStream.py
GRUSupportFns.ipynb		GRUSupportFns.ipynb
Gated Recurrent Unit.ipynb		Gated Recurrent Unit.ipynb
Graphs.ipynb		Graphs.ipynb
GroundTruth.py		GroundTruth.py
KalmanFilter.ipynb		KalmanFilter.ipynb
KalmanFilterTesting.ipynb		KalmanFilterTesting.ipynb
StreamClass.ipynb		StreamClass.ipynb
Tensorflow NN Tutorial.ipynb		Tensorflow NN Tutorial.ipynb
Using_Sensor_Fusion_and_Deep_Learning_to_Improve_Activity_Tracking.pdf		Using_Sensor_Fusion_and_Deep_Learning_to_Improve_Activity_Tracking.pdf
__init__.py		__init__.py
apikey.txt		apikey.txt
deep_learning.py		deep_learning.py
gru.log		gru.log
learning.log		learning.log
log.txt		log.txt
lprof0		lprof0
lstm.log		lstm.log
store_result.sh		store_result.sh

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I wrote my dissertation for my Computer Science degree on whether the accuracy of GPS tracking for sports activities (running, cycling, walking) could be improved using additional sensors found in a smartphone. Initially, I implemented a Kalman Filter to fuse the readings of each sensor, this was written in python and used popular libraries like N…

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