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.
P2lhap: Wearable-sensor-based human activity recognition, segmentation, and forecast through patch-to-label seq2seq transformer,
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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.