  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.
