Dataiku's Solution to SPHERE's Activity Recognition Challenge
  Our team won the second prize of the Safe Aging with SPHERE Challenge organized by SPHERE, in conjunction with ECML-PKDD and Driven Data. The goal of the competition was to recognize activities performed by humans, using sensor data. This paper presents our solution. It is based on a rich pre-processing and state of the art machine learning methods. From the raw train data, we generate a synthetic train set with the same statistical characteristics as the test set. We then perform feature engineering. The machine learning modeling part is based on stacking weak learners through a grid searched XGBoost algorithm. Finally, we use post-processing to smooth our predictions over time. 
Maxime Voisin, Leo Dreyfus-Schmidt, Pierre Gutierrez, Samuel Ronsin   and Marc Beillevaire
stat.ML cs.LG