Conditional Generative Adversarial Networks Based Inertial Signal Translation
This work addresses the need for efficient gait analysis using wrist sensors, but it is incremental as it applies existing GAN methods to a new data translation task.
The paper tackled the problem of translating inertial signals from a wrist-worn sensor to those of a shoe-mounted sensor using Conditional Generative Adversarial Networks (GANs), achieving accurate translation that enables everyday gait analysis.
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.