Conditional Generative Adversarial Networks Based Inertial Signal Translation

• URL: http://arxiv.org/abs/2509.00016v1
• Date: Sat, 16 Aug 2025 09:06:58 GMT
• Title: Conditional Generative Adversarial Networks Based Inertial Signal Translation
• Abstract summary: The paper presents an approach in which inertial signals measured with a wrist-worn sensor are translated into those that would be recorded using a shoe-mounted sensor.<n> Conditional Geneversarative Adrial Networks (GANs) are used to translate the signals.<n>Experiment results show that the proposed approach allows for an accurate translation, enabling the use of wrist sensor inertial signals for efficient, every-day gait analysis.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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