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

A Platform to Collect, Unify, and Distribute Inertial Labeled Signals for Human Activity Recognition