A large-scale benchmark of 17 WHAR models across 30 datasets finds predictive performance has plateaued while efficiency favors compact neural models and random forests on the Pareto frontier.
Learning from the best: Contrastive representations learning across sensor locations for wearable activity recognition
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
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.
EduGage releases a multimodal sensor dataset and models for estimating learner engagement in self-guided video learning, reporting MAE of 0.81 and outperforming baselines with 16 participants.
citing papers explorer
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WHAR Arena: Benchmarking the State of the Art in Efficient Wearable Human Activity Recognition
A large-scale benchmark of 17 WHAR models across 30 datasets finds predictive performance has plateaued while efficiency favors compact neural models and random forests on the Pareto frontier.
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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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EduGage: Methods and Dataset for Sensor-Based Momentary Assessment of Engagement in Self-Guided Video Learning
EduGage releases a multimodal sensor dataset and models for estimating learner engagement in self-guided video learning, reporting MAE of 0.81 and outperforming baselines with 16 participants.