Augmented-GPT: Leveraging Generative AI Agent for Synthesizing Biomechanical Features for Transformer Model

Abstract

Nowadays, data augmentation is playing a vital role in various deep learning applications, especially in biomechanical automatic analysis, where the features are hard to collect due to different sophisticated reasons. However, traditional data augmentation techniques expose their limitation by synthesizing unsuitable content that neglects the semantic, global content of the dataset. Furthermore, this problem even exacerbates the outputs of transformers, as these models normally require a large amount of data for training. Therefore, this study is conducted, introducing a new approach called Augmented-GPT that utilizes the GPT-4o as a generative AI agent and designs proper prompts to synthesize data, enriching the content of the dataset effectively by considering global content to create more relevant features for specific rare classes. As a result, our Augmented-GPT can assist the TabTransformer in reaching up to 81.0% in predicting the condition of orthopedic patients based on synthesized biomechanical features. This approach outperforms other traditional data augmentation approaches, opening a new direction to handle complex, rare occurrences in datasets for training complex deep learning models.

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