WavesFM: Hierarchical Representation Learning for Longitudinal Wearable Sensor Waveforms

ArXi:2605.09173v1 Announce Type: cross Wearable sensors enable the continuous acquisition of high-resolution physiological waveforms, such as photoplethysmography and accelerometry, under free-living conditions. However, inferring health-related phenotypes from these signals presents significant challenges due to high sampling frequencies, multimodal dependencies, and extreme sequence lengths (e.g., weeks of recordings), compounded by a scarcity of ground-truth labels.