Combining variational autoencoders with generative adversarial networks to adaptively adjust the electromagnetic compatibility of biomechanical data analysis platforms
Abstract
This study investigates the integration of variational autoencoders (VAEs) and generative adversarial networks (GANs) to enhance the electromagnetic compatibility (EMC) of biomechanical data analysis platforms. Leveraging a comprehensive dataset from multiple wearable devices, we capture diverse biomechanical parameters, including muscle activity, joint angles, and kinematic data. The preprocessing phase involves normalization and feature extraction, followed by encoding the biomechanical data into a latent space using VAEs. The GAN component generates synthetic data that are indistinguishable from real data, which are then utilized to adjust the EMC parameters of the analysis platform. Our results reveal significant improvements in model performance, as indicated by reduced mean squared error (MSE) and enhanced structural similarity index (SSIM) across multiple training epochs. Furthermore, the EMC adjustment process effectively minimizes electromagnetic interference, as evidenced by a substantial decrease in electromagnetic interference error function values. The high similarity between real and synthetic data validates the quality of the generated data. This integrated VAE-GAN framework presents a promising methodology for augmenting the accuracy and reliability of biomechanical data analysis in various applications.
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