arXiv AI
CHEAR: Confusion-Hierarchy Evidence-Aware Reasoning for Cost-Efficient Human Activity Recognition
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TRACE-TS: Attribution-Grounded and Traceable Sensor-Language Reasoning for Human Activity Understanding
arXiv:2608. 00200v1 Announce Type: cross Abstract: Wearable sensors capture fine-grained motion patterns that support rich behavioral understanding, yet most existing methods reduce these signals to activity labels.
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RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment
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RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment
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ActivityNarrated: An Open-Ended Narrative Paradigm for Wearable Human Activity Understanding
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OntoAR-HAR: Ontology-Guided Agentic RAG with Personalized Memory for Human Activity Recognition
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Closing the Modality Gap in Zero-Shot HAR: Contrastive Training and Separability-Optimized Prototypes on IMU Data
Zero-shot learning (ZSL) for inertial measurement unit (IMU)-based human activity recognition (HAR) faces a central challenge: bridging the gap between sensor embeddings and semantic class representations. We systematically evaluate seven configurations combining three inference methods with two training pipelines on the PAMAP2 dataset, using 14 seen and 4 unseen activity classes with subjects 108 and 109 held out for testing.