# Scaling Wearable Foundation Models Source: [Scaling Wearable Foundation Models]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:29:07.731Z Authors: Girish Narayanswamy, Xin Liu, Kumar Ayush, Yuzhe Yang, Xuhai Xu, Shun Liao, Jake Garrison, Shyam Tailor, Jake Sunshine, Yun Liu, Tim Althoff, Shrikanth Narayanan, Pushmeet Kohli, Jiening Zhan, Mark Malhotra, Shwetak Patel, Samy Abdel-Ghaffar, Daniel McDuff Published: 2024-10-17T15:08:21.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Condition Diagnosis (COPD) on Longitudinal Wearable Sensor Waveforms Downstream Tasks WavesFM](): Condition Diagnosis (COPD); Longitudinal Wearable Sensor Waveforms Downstream Tasks WavesFM; AUROC: 74.9. - [Lifestyle Factor Prediction (Frequent Sugar) on Longitudinal Wearable Sensor Waveforms Downstream Tasks WavesFM](): Lifestyle Factor Prediction (Frequent Sugar); Longitudinal Wearable Sensor Waveforms Downstream Tasks WavesFM; AUROC: 69.4. - [Active Smoker Prediction on Longitudinal Wearable Sensor Waveforms Downstream Tasks WavesFM](): Active Smoker Prediction; Longitudinal Wearable Sensor Waveforms Downstream Tasks WavesFM; AUROC: 72.2. - [Condition Diagnosis (Diabetes) on Longitudinal Wearable Sensor Waveforms WavesFM (Downstream Tasks)](): Condition Diagnosis (Diabetes); Longitudinal Wearable Sensor Waveforms WavesFM (Downstream Tasks); AUROC: 79.6. - [Condition Diagnosis (Stroke) on Longitudinal Wearable Sensor Waveforms Downstream Tasks WavesFM](): Condition Diagnosis (Stroke); Longitudinal Wearable Sensor Waveforms Downstream Tasks WavesFM; AUROC: 62.3. - [Treatment Identification (Nitrates) on Longitudinal Wearable Sensor Waveforms Downstream Tasks WavesFM](): Treatment Identification (Nitrates); Longitudinal Wearable Sensor Waveforms Downstream Tasks WavesFM; AUROC: 0.805. - [Condition Diagnosis (Atrial Fibrillation) on Longitudinal Wearable Sensor Waveforms Downstream Tasks WavesFM](): Condition Diagnosis (Atrial Fibrillation); Longitudinal Wearable Sensor Waveforms Downstream Tasks WavesFM; AUROC: 0.791. - [Condition Diagnosis (Cancer) on Longitudinal Wearable Sensor Waveforms WavesFM](): Condition Diagnosis (Cancer); Longitudinal Wearable Sensor Waveforms WavesFM; AUROC: 0.673. - [Obesity Prediction on Longitudinal Wearable Sensor Waveforms Downstream Tasks WavesFM](): Obesity Prediction; Longitudinal Wearable Sensor Waveforms Downstream Tasks WavesFM; AUROC: 0.817. - [Older Age Prediction on Longitudinal Wearable Sensor Waveforms WavesFM (Downstream Tasks)](): Older Age Prediction; Longitudinal Wearable Sensor Waveforms WavesFM (Downstream Tasks); AUROC: 0.881. - [Sex Male Prediction on Longitudinal Wearable Sensor Waveforms Downstream Tasks WavesFM](): Sex Male Prediction; Longitudinal Wearable Sensor Waveforms Downstream Tasks WavesFM; AUROC: 0.975. - [Treatment Identification (Diuretics) on Longitudinal Wearable Sensor Waveforms Downstream Tasks WavesFM](): Treatment Identification (Diuretics); Longitudinal Wearable Sensor Waveforms Downstream Tasks WavesFM; AUROC: 0.727. ## Original sources and implementation [Original paper]() [PDF]()