  Human activity recognition (HAR) is a very active research field. Recently,
deep learning techniques are being exploited to recognize human activities from
inertial signals. However, to compute accurate and reliable deep learning
models, a huge amount of data is required. The goal of our work is the
definition of a platform to support long-term data collection to be used in
training of HAR algorithms. The platform aims to integrate datasets of inertial
signals in order to make available to the scientific community a large dataset
of homogeneous data. The architecture of the platform has been defined and some
of the main components have been developed in order to verify the soundness of
the approach.
