SensingAgents: A Multi-Agent Collaborative Framework for Robust IMU Activity Recognition

TL;DR

SensingAgents introduces a multi-agent LLM-based framework for robust human activity recognition using IMU sensors, significantly improving accuracy and handling sensor conflicts better than existing models.

Contribution

The paper presents a novel multi-agent system leveraging LLMs for IMU-based activity recognition, enhancing robustness and interpretability over prior single-agent and deep learning approaches.

Findings

Achieves 79.5% accuracy on Shoaib dataset, 29% higher than previous agent models.

Outperforms deep learning baselines by 9.4% in complex noisy scenarios.

Demonstrates robustness in conflicting or noisy multi-sensor data conditions.

Abstract

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), a pair of Advocate Agents that resolves sensor conflicts through dynamic and static dialectical debates, and a Decision Agent that ensures reliability under sensor drift or failure. Evaluation on the Shoaib dataset demonstrates that SensingAgents significantly outperforms state-of-the-art single-agent and multi-agent LLM models, achieving an accuracy of 79.5% in a zero setting--29% higher than existing agent models and 9.4% higher than deep learning baselines--particularly in complex scenarios where multi-sensor data is conflicting or noisy. Our work highlights the potential of multi-agent collaborative reasoning for advancing the robustness and interpretability of ubiquitous sensing systems.