Coaching and analysis
We leverage a dataset with 40 million hours of wearable knowledge sampled from over 60,000 members in the course of the interval from March to Could 2024. The dataset was completely anonymized or de-identified to make sure that participant data was eliminated and privateness was maintained. Topics wore a wide range of Fitbit and Google Pixel smartwatches and trackers and consented for his or her knowledge for use for analysis and improvement of recent well being and wellness services. The themes have been requested to self-report intercourse, age, and weight.
To pre-train LSM-2, we make use of the AIM SSL approach launched within the earlier part. AIM implements a masked reconstruction coaching goal, and learns to grasp knowledge that’s naturally lacking, and impute knowledge that’s artificially masked. This unified framework permits LSM-2 to be taught the underlying construction (together with missingness) inherent in wearable sensor knowledge.
We curate a set of downstream duties to judge the pre-trained mannequin, utilizing meta-data that was collected alongside the sensor alerts for the needs of analysis and improvement. These embrace person annotated actions from a set of 20 completely different classes (comparable to operating, snowboarding, kayaking and enjoying golf) and self-reported diagnoses of hypertension and nervousness. These knowledge have been cut up into fine-tuning and analysis units the place knowledge from every particular person was solely in both the tuning or the analysis set and never each. Information from people used within the pretraining stage was additionally not included within the fine-tuning or analysis phases.
The generative capabilities of LSM-2 are evaluated via the duties 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 through linear probe on numerous discriminative duties. Particularly we gauge the applicability of the LSM-2 embeddings to the duties of binary hypertension classification, binary nervousness classification, and 20-class exercise recognition. We consider LSM-2’s means to mannequin physiology through age and BMI regression duties.
