REVIEW 7 cited by
Scaling Wearable Foundation Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Wearable sensors have become ubiquitous thanks to a variety of health tracking features. The resulting continuous and longitudinal measurements from everyday life generate large volumes of data; however, making sense of these observations for scientific and actionable insights is non-trivial. Inspired by the empirical success of generative modeling, where large neural networks learn powerful representations from vast amounts of text, image, video, or audio data, we investigate the scaling properties of sensor foundation models across compute, data, and model size. Using a dataset of up to 40 million hours of in-situ heart rate, heart rate variability, electrodermal activity, accelerometer, skin temperature, and altimeter per-minute data from over 165,000 people, we create LSM, a multimodal foundation model built on the largest wearable-signals dataset with the most extensive range of sensor modalities to date. Our results establish the scaling laws of LSM for tasks such as imputation, interpolation and extrapolation, both across time and sensor modalities. Moreover, we highlight how LSM enables sample-efficient downstream learning for tasks like exercise and activity recognition.
Forward citations
Cited by 7 Pith papers
-
A Diffusion-Model Subpopulation Digital Twin for Mobile Health Deployment: A Case Study on the HeartSteps Intervention
A pre-train/fine-tune/calibrate diffusion-model pipeline produces subpopulation digital twins that out-reproduce simpler simulators on temporal and between-participant structure in a HeartSteps replay.
-
Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series
Across 14 sensor generation settings, flow-matching models are the strongest overall baseline, while demographic conditioning, time-frequency modeling, and moderate synthetic augmentation improve hard regimes and down...
-
OSF: On Pre-training and Scaling of Sleep Foundation Models
Channel-masked self-supervised pretraining on a 166,500-hour multi-source sleep corpus yields OSF, which generalizes better to missing channels and scales with data and model size.
-
Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions
A foundation model of wearable behavioral data outperforms simple baselines and complements a PPG sensor model across 57 health detection tasks.
-
LSM-2: Learning from Incomplete Wearable Sensor Data
LSM-2 with Adaptive and Inherited Masking learns usable representations directly from incomplete day-long wearable data, outperforming imputation-based baselines on most tasks.
-
Human Centric Embodied Intelligence for Soft Wearable Robotics
A narrative review introducing Human-Centric Embodied Intelligence (HCEI) and the PCAA framework as organizing principles for soft wearable robotics.
-
Cross-device Zero-shot Label Transfer via Alignment of Time Series Foundation Model Embeddings
A framework using adversarial alignment of frozen time-series foundation model embeddings transfers labels to a simulated target domain, but the target is synthetic and no real consumer device data is tested.
Discussion (0). Continue with ORCID to comment.