This research introduces a novel framework that enables LLMs to understand human movement from privacy-preserving millimeter-wave radar data.

• Motion-guided radar tokenizer converts sparse point clouds into language-compatible tokens
• Multimodal LLM architecture bridges the gap between radar signals and semantic understanding
• Privacy-preserving solution for healthcare monitoring without capturing identifiable visual information
• Real-time capabilities with potential applications in patient monitoring and activity recognition

This breakthrough matters for healthcare settings where continuous monitoring is beneficial but privacy concerns limit camera-based solutions, enabling unobtrusive patient activity tracking and fall detection.

RadarLLM: Empowering Large Language Models to Understand Human Motion from Millimeter-wave Point Cloud Sequence