SensingAgents is a multi-agent LLM framework that reaches 79.5% zero-shot accuracy on IMU activity recognition by using position-specific analysts, debating advocates, and a final decision agent, beating prior agent and deep-learning baselines.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
GenHAR generalizes cross-domain human activity recognition by 9.97% accuracy and 6.4x lower FLOPs via tokenized sensor data, frequency channel correlations, selective masking, and efficient attention, with deployment detecting 2.15 billion activities.
citing papers explorer
-
SensingAgents: A Multi-Agent Collaborative Framework for Robust IMU Activity Recognition
SensingAgents is a multi-agent LLM framework that reaches 79.5% zero-shot accuracy on IMU activity recognition by using position-specific analysts, debating advocates, and a final decision agent, beating prior agent and deep-learning baselines.
-
GenHAR: Generalizing Cross-domain Human Activity Recognition for Last-mile Delivery
GenHAR generalizes cross-domain human activity recognition by 9.97% accuracy and 6.4x lower FLOPs via tokenized sensor data, frequency channel correlations, selective masking, and efficient attention, with deployment detecting 2.15 billion activities.