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Activity Detection from Wearable Devices (thesis)


Description Wearable devices enable researchers to unobtrusively collect a rich array of information into peoples lifestyles and habits. Combining this rich data resource with machine learning analysis can lead to robust techniques to objectively quantify and log a range of different activities. 
Task Explore the effectiveness of different machine learning techniques in performing activity detection based on wearable signals.
Utilises Python: scikit-learn up to Tensorflow/Keras
Requirements Prior machine learning knowledge related programming skills a plus
Languages English
Supervisor Dr. Nicholas Cummins (nicholas.cummins@informatik.uni-augsburg.de)