activity
20232026
most citedLarge-scale Training of Foundation Models for Wearable Biosignals

23 citations · 23 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2026

MS-ECG-FM: Towards a More Universal Electrocardiogram Foundation Model for Health Monitoring using Multi-source Contrastive Learning

Robert A. Lewis, I-Min Chiu, Kyle Verrier +7

Electrocardiography (ECG) records the electrical activity of the heart, aiding diagnosis by detecting abnormalities in cardiac function. ECG foundation models have demonstrated pro…

cs.LG2026

Emergent Symbolic Structure in Health Foundation Models: Extraction, Alignment, and Cross-Modal Transfer

Gajendra Katuwal, Advait Koparkar, Salar Abbaspourazad +2

We show that information can be transferred post-hoc across independently trained health foundation models (FMs), each pretrained on ~20M minutes of wearable sensor data from ~172K…

cs.LG2025

Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions

Eray Erturk, Fahad Kamran, Salar Abbaspourazad +6

Wearable devices record physiological and behavioral signals that can improve health predictions. While foundation models are increasingly used for such predictions, they have been…

cs.LG2025

Wearable Accelerometer Foundation Models for Health via Knowledge Distillation

Salar Abbaspourazad, Anshuman Mishra, Joseph Futoma +2

Modern wearable devices can conveniently record various biosignals in the many different environments of daily living, enabling a rich view of individual health. However, not all b…

cs.LG2023★ 23 cited

Large-scale Training of Foundation Models for Wearable Biosignals

Salar Abbaspourazad, Oussama Elachqar, Andrew C. Miller +3

Tracking biosignals is crucial for monitoring wellness and preempting the development of severe medical conditions. Today, wearable devices can conveniently record various biosigna…