Wavelet-Driven Masked Multiscale Reconstruction for PPG Foundation Models

ICLR 2026 Conference Submission21669 Authors

Keywords: Wearables, Foundation Models, Masked Reconstruction, Discrete Wavelet Decomposition, Self Supervised Learning

TL;DR: Masked multiscale reconstruction leverages multi-resolution wavelet decomposition to pretrain a large-scale wavelet-driven PPG foundation model, learning rich time–frequency features and transferring with superior performance across health tasks

Abstract:

Wearable foundation models have the potential to transform digital health by learning transferable representations from large-scale biosignals collected in everyday settings.
While recent progress has been made in large-scale pretraining, most approaches overlook the spectral structure of photoplethysmography (PPG) signals, wherein physiological rhythms unfold across multiple frequency bands.
Motivated by the insight that many downstream health-related tasks depend on multi-resolution features spanning fine-grained waveform morphology to global rhythmic dynamics, we introduce Masked Multiscale Reconstruction (MMR) for PPG representation learning -- a self-supervised pretraining framework that explicitly learns from hierarchical time–frequency scales of PPG data.
The pretraining task is designed to reconstruct randomly masked out coefficients obtained from a wavelet-based multiresolution decomposition of PPG signals, forcing the transformer encoder to integrate information across temporal and spectral scales.
We pretrain our model with MMR using ~17 million unlabeled 10-second PPG segments collected from over ~32000 smartwatch users largely in naturalistic field settings, ensuring high variability and ecological validity.
On 11 of 13 diverse health-related tasks, MMR trained on large-scale wearable PPG data outperforms or matches state-of-the-art open-source PPG foundation models, time-series foundation models and other self-supervised baselines.
Extensive analysis of our learned embeddings and systematic ablations underscore the value of wavelet-based representations, showing that they capture robust and physiologically-grounded features.
Together, these results highlight the potential of MMR as a step toward generalizable PPG foundation models.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 21669