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

arXiv: https://arxiv.org/abs/2605.04608 · HTML: https://mlsanity.com/paper/2605.04608/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 6 May 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 8 of 8)

- [Large Language Models Hallucination: A Comprehensive Survey](https://mlsanity.com/paper/2510.06265/) (2025)
- [IoT-LLM: a framework for enhancing Large Language Model reasoning from real-world sensor data](https://mlsanity.com/paper/2410.02429/) (2024)
- [Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and Challenges](https://mlsanity.com/paper/2407.07196/) (2024)
- [LLaSA: A Sensor-Aware LLM for Natural Language Reasoning of Human Activity from IMU Data](https://mlsanity.com/paper/2406.14498/) (2024)
- [Using LLMs for Late Multimodal Sensor Fusion for Activity Recognition](https://mlsanity.com/paper/2509.10729/) (2025)
- [ConSensus: Multi-Agent Collaboration for Multimodal Sensing](https://mlsanity.com/paper/2601.06453/) (2026)
- [Active Learning for WBAN-based Health Monitoring](https://mlsanity.com/paper/2408.02849/) (2024)
- [mmExpert: Integrating Large Language Models for Comprehensive mmWave Data Synthesis and Understanding](https://mlsanity.com/paper/2509.16521/) (2025)

## Abstract (arXiv)

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.