mmExpert: Integrating Large Language Models for Comprehensive mmWave Data Synthesis and Understanding
This work addresses data scarcity for researchers and developers in human-centric mmWave applications, though it is incremental as it applies existing LLM capabilities to a new domain.
The paper tackles the high cost of data acquisition and annotation in millimeter-wave sensing by introducing mmExpert, a framework that uses large language models to generate synthetic datasets, enabling zero-shot generalization in real-world environments and significantly enhancing downstream model performance.
Millimeter-wave (mmWave) sensing technology holds significant value in human-centric applications, yet the high costs associated with data acquisition and annotation limit its widespread adoption in our daily lives. Concurrently, the rapid evolution of large language models (LLMs) has opened up opportunities for addressing complex human needs. This paper presents mmExpert, an innovative mmWave understanding framework consisting of a data generation flywheel that leverages LLMs to automate the generation of synthetic mmWave radar datasets for specific application scenarios, thereby training models capable of zero-shot generalization in real-world environments. Extensive experiments demonstrate that the data synthesized by mmExpert significantly enhances the performance of downstream models and facilitates the successful deployment of large models for mmWave understanding.