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.
Spatial-temporal masked autoencoder for multi-device wearable human activity recognition
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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CLMM is a two-stage contrastive learning framework using CNN-DiffTransformer encoders and dual-branch fusion to improve multimodal human activity recognition under limited labels.
citing papers explorer
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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.
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Contrastive Learning for Multimodal Human Activity Recognition with Limited Labeled Data
CLMM is a two-stage contrastive learning framework using CNN-DiffTransformer encoders and dual-branch fusion to improve multimodal human activity recognition under limited labels.