# Conditional Generative Adversarial Networks Based Inertial Signal Translation

## TL;DR

This paper introduces a method using Conditional GANs to translate wrist-worn inertial signals into shoe-mounted sensor signals, facilitating gait analysis with existing wearable devices.

## Contribution

It demonstrates the effectiveness of GAN-based translation models for inertial signal conversion, enabling gait analysis without specialized sensors.

## Key findings

- Wasserstein GANs outperform traditional GANs in translation accuracy.
- Convolutional U-Net architecture yields better results than autoencoders.
- The approach enables accurate gait analysis using wrist-worn sensors.

## Abstract

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.