A single shared model performs human activity recognition on arbitrary sensor channel configurations by combining independent channel encoding with metadata-conditioned late fusion and joint optimization.
Har-doremi: Optimizing data mixture for self-supervised human activity recognition across heterogeneous imu datasets
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The survey organizes foundation models for sensor-based HAR into a lifecycle taxonomy and identifies three trajectories: HAR-specific models from scratch, adaptation of general time-series models, and integration with large language models.
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Channel-Free Human Activity Recognition via Inductive-Bias-Aware Fusion Design for Heterogeneous IoT Sensor Environments
A single shared model performs human activity recognition on arbitrary sensor channel configurations by combining independent channel encoding with metadata-conditioned late fusion and joint optimization.
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Foundation Models Defining A New Era In Sensor-based Human Activity Recognition: A Survey And Outlook
The survey organizes foundation models for sensor-based HAR into a lifecycle taxonomy and identifies three trajectories: HAR-specific models from scratch, adaptation of general time-series models, and integration with large language models.