W-JEPA: Wavelet Joint-Embedding Predictive Architecture for Wearable Activity Recognition

Key points

• A major challenge in wearable device activity recognition is the heterogeneity across datasets in sensor placement, channel count, sampling rate and subjects.1
• W-JEPA predicts masked token representations supplied by an exponential-moving-average (EMA) target encoder.1
• Across six wearable activity benchmarks, W-JEPA outperforms state-of-the-art methods under random-window splits and remains the strongest when subjects are held out, improving average macro-F1 by 8.7 percentage points over the strongest published baseline.1
• These results indicate that physically grounded multiscale tokenization, combined together with latent predictive learning, provides a promising foundation for transferable representations of heterogeneous wearable inertial data.1

Sources (1)

• [1]W-JEPA: Wavelet Joint-Embedding Predictive Architecture for Wearable Activity Recognition
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 01:58 AM

  We introduce W-JEPA, a self-supervised framework that exploits the multiscale structure of inertial motion and leverages a joint-embedding predictive architecture, by organizing windows into a fixed grid of wavelet tokens indexed by scale, sensor axis, and time.

  A major challenge in wearable device activity recognition is the heterogeneity across datasets in sensor placement, channel count, sampling rate and subjects.

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