Learning from incomplete wearable sensor data

Training and evaluation

We leverage a dataset with 40 million hours of wearable data sampled from over 60,000 participants during the period from March to May 2024. The dataset was thoroughly anonymized or de-identified to ensure that participant information was removed and privacy was maintained. Subjects wore a variety of Fitbit and Google Pixel smartwatches and trackers and consented for their data to be used for research and development of new health and wellness products and services. The subjects were asked to self-report sex, age, and weight.

To pre-train LSM-2, we employ the AIM SSL technique introduced in the previous section. AIM implements a masked reconstruction training objective, and learns to understand data that is naturally missing, and impute data that is artificially masked. This unified framework allows LSM-2 to learn the underlying structure (including missingness) inherent in wearable sensor data.

We curate a set of downstream tasks to evaluate the pre-trained model, using meta-data that was collected alongside the sensor signals for the purposes of research and development. These include user annotated activities from a set of 20 different categories (such as running, skiing, kayaking and playing golf) and self-reported diagnoses of hypertension and anxiety. These data were split into fine-tuning and evaluation sets where data from each individual was only in either the tuning or the evaluation set and not both. Data from individuals used in the pretraining stage was also not included in the fine-tuning or evaluation stages.

The generative capabilities of LSM-2 are evaluated through the tasks of random imputation, temporal interpolation, temporal extrapolation (forecasting), and sensor imputation, described in our LSM-1 work.

The utility of the LSM-2 embeddings are evaluated via linear probe on a number of discriminative tasks. Specifically we gauge the applicability of the LSM-2 embeddings to the tasks of binary hypertension classification, binary anxiety classification, and 20-class activity recognition. We evaluate LSM-2’s ability to model physiology via age and BMI regression tasks.