3D human pose data augmentation using Generative Adversarial Networks for robotic-assisted movement quality assessment
Xuefeng Wang,
Yang Mi,
Xiang Zhang
Abstract:In the realm of human motion recognition systems, the augmentation of 3D human pose data plays a pivotal role in enriching and enhancing the quality of original datasets through the generation of synthetic data. This augmentation is vital for addressing the current research gaps in diversity and complexity, particularly when dealing with rare or complex human movements. Our study introduces a groundbreaking approach employing Generative Adversarial Networks (GANs), coupled with Support Vector Machine (SVM) and… Show more
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