SensorFM: Towards a general intelligence and interface for wearable health data

SensorFM is a large foundation model designed to turn wearable-device signals into a reusable representation of human physiology. Trained on more than one trillion minutes of multimodal data from five million people, it performs well across 35 health-related prediction tasks while requiring relatively few labeled examples. The authors argue that scaling both data and model size enables a general-purpose interface for wearable health data rather than isolated, task-specific models.

The challenge of wearable health data

• Wearables continuously capture heart rate, movement, temperature, blood oxygen, sleep, and related signals.
• Interpreting these measurements is difficult because:
  • Baseline physiology and lifestyle vary substantially between individuals.
  • Reliable labels such as diagnoses, lab results, and validated questionnaires are costly and difficult to collect.
  • Traditional models usually target one health outcome at a time and generalize poorly.

Training on more than a trillion minutes

• SensorFM was trained on de-identified data from five million consenting participants collected between September 2024 and September 2025.
• The dataset includes:
  • More than 100 countries and all 50 U.S. states.
  • Over 20 Fitbit and Pixel Watch models.
  • More than two billion sensor-hours of data.
• The model processes 34 minute-level features from:
  • PPG
  • Accelerometry
  • Electrodermal activity
  • Skin temperature
  • Altimetry
• These signals represent heart rate, heart-rate variability, blood oxygen, sleep, movement, skin conductance, and temperature across full days.

Learning from incomplete sensor recordings

• SensorFM uses self-supervised masked reconstruction rather than relying on medical labels.
• Its Adaptive and Inherited Masking framework treats naturally missing data as part of the learning problem.
• This avoids:
  • Imputing gaps, which can introduce bias.
  • Discarding incomplete windows, which wastes real-world data.
• The resulting representation is explicitly aware of missingness and can learn from fragmented wearable recordings.

Scaling data and model capacity

• Experiments varied training data from roughly two million to two billion sensor-hours and model size from 100,000 to 100 million parameters.
• Larger models trained on more data consistently improved both reconstruction and downstream health prediction.
• The largest model:
  • Reduced reconstruction loss by 31% compared with the smallest version.
  • Improved classification performance by an average of 9% in AUC.
  • Improved regression performance by 21% in Pearson correlation.
  • Won on 33 of 35 downstream tasks.
• Scaling data and model size together produced near-linear gains with no observed saturation.

One representation across many health domains

• SensorFM was tested on 35 tasks from three prospective studies involving 13,985 participants.
• The tasks covered:
  • Cardiovascular health
  • Metabolic risk
  • Mental health
  • Sleep
  • Demographics
  • Lifestyle
• With the encoder frozen and only a lightweight linear head trained, SensorFM embeddings outperformed engineered-feature supervised baselines on 34 of 35 tasks.
• Larger models appeared to learn physiologically relevant demographic and individual differences without being explicitly given demographic inputs.
• The model showed particular value for difficult-to-measure conditions such as depression and anxiety.
• It also reached strong performance with relatively small quantities of labeled data, addressing a major constraint in healthcare modeling.

Automated adaptation through an agentic “classroom”

• The authors introduce a collaborative system of LLM agents intended to automate the creation of prediction heads.
• This approach aims to reduce the manual work traditionally required for:
  • Feature engineering
  • Architecture selection
  • Hyperparameter tuning
• The supplied article excerpt ends while introducing this system, so its detailed workflow and results are not described here.

SensorFM demonstrates that large-scale, missingness-aware pre-training can produce a broadly useful representation of wearable physiology. Its strongest practical promise is label-efficient adaptation across many health applications, potentially providing a foundation for personalized health agents and more general wearable-data interfaces.