# Conditional Generative Adversarial Networks Based Inertial Signal Translation

arXiv: https://arxiv.org/abs/2509.00016 · HTML: https://mlsanity.com/paper/2509.00016/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 16 Aug 2025.

## Builds on (most cited first, 2 of 2)

- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [cGAN-Based High Dimensional IMU Sensor Data Generation for Enhanced Human Activity Recognition in Therapeutic Activities](https://mlsanity.com/paper/2302.07998/) (2023)

## Abstract (arXiv)

The paper presents an approach in which inertial signals measured with a wrist-worn sensor (e.g., a smartwatch) are translated into those that would be recorded using a shoe-mounted sensor, enabling the use of state-of-the-art gait analysis methods. In the study, the signals are translated using Conditional Generative Adversarial Networks (GANs). Two different GAN versions are used for experimental verification: traditional ones trained using binary cross-entropy loss and Wasserstein GANs (WGANs). For the generator, two architectures, a convolutional autoencoder, and a convolutional U-Net, are tested. The experiment results have shown that the proposed approach allows for an accurate translation, enabling the use of wrist sensor inertial signals for efficient, every-day gait analysis.