SensorFM: Towards a general intelligence and interface for wearable health data (opens in new tab)
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.
