# Homogenization of Existing Inertial-Based Datasets to Support Human Activity Recognition

arXiv: https://arxiv.org/abs/2201.07891 · HTML: https://mlsanity.com/paper/2201.07891/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 17 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Benchmark Analysis of Representative Deep Neural Network Architectures](https://mlsanity.com/paper/1810.00736/) (2018)
- [Falls as anomalies? An experimental evaluation using smartphone accelerometer data](https://mlsanity.com/paper/1507.01206/) (2015)

## Built on by (most cited first, 1 of 1)

- [Unsupervised Deep Learning-based clustering for Human Activity Recognition](https://mlsanity.com/paper/2211.05483/) (2022)

## Abstract (arXiv)

Several techniques have been proposed to address the problem of recognizing activities of daily living from signals. Deep learning techniques applied to inertial signals have proven to be effective, achieving significant classification accuracy. Recently, research in human activity recognition (HAR) models has been almost totally model-centric. It has been proven that the number of training samples and their quality are critical for obtaining deep learning models that both perform well independently of their architecture, and that are more robust to intraclass variability and interclass similarity. Unfortunately, publicly available datasets do not always contain hight quality data and a sufficiently large and diverse number of samples (e.g., number of subjects, type of activity performed, and duration of trials). Furthermore, datasets are heterogeneous among them and therefore cannot be trivially combined to obtain a larger set. The final aim of our work is the definition and implementation of a platform that integrates datasets of inertial signals in order to make available to the scientific community large datasets of homogeneous signals, enriched, when possible, with context information (e.g., characteristics of the subjects and device position). The main focus of our platform is to emphasise data quality, which is essential for training efficient models.