WavesFM uses hierarchical SSL to pretrain a segment encoder on short waveforms followed by a temporal encoder on multi-day sequences, outperforming prior methods on 58 tasks after training on over 12 million hours of data from hundreds of thousands of people.
Relcon: Relative contrastive learning for a motion foundation model for wearable data
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BenchHAR finds that hybrid reconstruction-plus-contrastive SSL with CNN encoders generalizes best for sensor HAR but overall performance on unseen distributions remains unsatisfactory.
citing papers explorer
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WavesFM: Hierarchical Representation Learning for Longitudinal Wearable Sensor Waveforms
WavesFM uses hierarchical SSL to pretrain a segment encoder on short waveforms followed by a temporal encoder on multi-day sequences, outperforming prior methods on 58 tasks after training on over 12 million hours of data from hundreds of thousands of people.
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BenchHAR: Benchmarking Self-Supervised Learning for Generalizable Sensor-based Activity Recognition
BenchHAR finds that hybrid reconstruction-plus-contrastive SSL with CNN encoders generalizes best for sensor HAR but overall performance on unseen distributions remains unsatisfactory.