SensingAgents: A Multi-Agent Collaborative Framework for Robust IMU Activity Recognition
Human Activity Recognition (HAR) using Inertial Measurement Unit (IMU) sensors is a cornerstone of mobile health, smart environments, and human-computer interaction. However, current deep learning-based HAR models often struggle with heavy reliance on labeled data, position-specific ambiguity, and a lack of transparent reasoning. Inspired by the advanced agents framework, which emulates a collaborative agent using Large Language Models (LLMs), we propose SensingAgents, a novel multi-agent system for robust IMU activity recognition. SensingAgents organizes LLM-powered agents into specialized roles: a group of Analyst Agents for position-specific sensor analysis (arm, wrist, belt, pocket), ...
| Authors | Naiyu Zheng, Tianlong Yu, Haochen Yin et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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