{"ID":2881641,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.00016","arxiv_id":"2509.00016","title":"Conditional Generative Adversarial Networks Based Inertial Signal Translation","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.","short_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...","url_abs":"https://arxiv.org/abs/2509.00016","url_pdf":"https://arxiv.org/pdf/2509.00016v1","authors":"[\"Marcin Kolakowski\"]","published":"2025-08-16T09:06:58Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.LG\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}