AnyMo pre-trains a graph encoder on physics-simulated multi-placement IMU data and aligns full-body motion tokens with LLMs to enable zero-shot activity recognition, retrieval, and captioning across unseen datasets and setups.
Past, present, and future of sensor-based human activity recognition using wearables: A surveying tutorial on a still challenging task.Proc
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TCNet modulates handcrafted feature anchors with neural context from raw signals to achieve higher mF1 scores on five HAR benchmarks than prior methods like rTsfNet.
citing papers explorer
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AnyMo: Geometry-Aware Setup-Agnostic Modeling of Human Motion in the Wild
AnyMo pre-trains a graph encoder on physics-simulated multi-placement IMU data and aligns full-body motion tokens with LLMs to enable zero-shot activity recognition, retrieval, and captioning across unseen datasets and setups.
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Feature Anchors for Time-Series Sensor-Based Human Activity Recognition
TCNet modulates handcrafted feature anchors with neural context from raw signals to achieve higher mF1 scores on five HAR benchmarks than prior methods like rTsfNet.