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

## TL;DR

This paper presents a platform designed to collect, unify, and distribute large, homogeneous inertial signal datasets to enhance the training of deep learning models for human activity recognition.

## Contribution

It introduces a novel platform architecture that supports long-term data collection and integration of inertial datasets for HAR research.

## Key findings

- Platform architecture has been defined and main components developed.
- The platform facilitates large-scale, homogeneous data collection.
- Initial verification shows the approach is sound.

## Abstract

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.