# Transformer Networks for Data Augmentation of Human Physical Activity Recognition

arXiv: https://arxiv.org/abs/2109.01081 · HTML: https://mlsanity.com/paper/2109.01081/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 2 Sep 2021.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)
- [Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case](https://mlsanity.com/paper/2001.08317/) (2020)
- [On Attention Models for Human Activity Recognition](https://mlsanity.com/paper/1805.07648/) (2018)
- [Human Activity Recognition from Wearable Sensor Data Using Self-Attention](https://mlsanity.com/paper/2003.09018/) (2020)
- [GANBERT: Generative Adversarial Networks with Bidirectional Encoder Representations from Transformers for MRI to PET synthesis](https://mlsanity.com/paper/2008.04393/) (2020)
- [Towards Deep Clustering of Human Activities from Wearables](https://mlsanity.com/paper/2008.01659/) (2020)

## Built on by (most cited first, 2 of 2)

- [Transformers in Healthcare: A Survey](https://mlsanity.com/paper/2307.00067/) (2023)
- [Physically Plausible Data Augmentations for Wearable IMU-based Human Activity Recognition Using Physics Simulation](https://mlsanity.com/paper/2508.13284/) (2025)

## Abstract (arXiv)

Data augmentation is a widely used technique in classification to increase data used in training. It improves generalization and reduces amount of annotated human activity data needed for training which reduces labour and time needed with the dataset. Sensor time-series data, unlike images, cannot be augmented by computationally simple transformation algorithms. State of the art models like Recurrent Generative Adversarial Networks (RGAN) are used to generate realistic synthetic data. In this paper, transformer based generative adversarial networks which have global attention on data, are compared on PAMAP2 and Real World Human Activity Recognition data sets with RGAN. The newer approach provides improvements in time and savings in computational resources needed for data augmentation than previous approach.