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AllTopicsToday > Blog > AI > Learning from incomplete wearable sensor data
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AI

Learning from incomplete wearable sensor data

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Last updated: July 26, 2025 4:32 am
AllTopicsToday
Published: July 26, 2025
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Coaching and analysis

In the course of the interval from March to Could 2024, we utilized a dataset containing 40 million hours of wearable information sampled from over 60,000 members. Topics wore quite a lot of Fitbit and Google Pixel smartwatches and trackers, and agreed that the info can be used to develop new well being and wellness services. Topics had been requested to self-report their intercourse, age and weight.

To pre-train the LSM-2, we make use of the AIM SSL expertise launched within the earlier part. AIM will implement the targets of masked reconstruction coaching, perceive information that’s lacking in nature, and study to help artificially masked information. This unified framework permits LSM-2 to study the underlying constructions (together with lacking) particular to wearable sensor information.

A collection of downstream duties are curated to guage pre-trained fashions utilizing metadata collected together with sensor alerts for analysis and improvement functions. These embrace user-annotated actions from 20 completely different classes (operating, snowboarding, kayaking, enjoying golf, and so forth.), in addition to self-report analysis of hypertension and anxiousness. These information had been break up into fine-tuning and analysis units, both in tuning or analysis units, fairly than each. Information from people used in the course of the pre-deletion stage had been additionally not included within the fine-tuning or analysis stage.

The era potential of LSM-2 is assessed by means of the duties of random task, temporal interpolation, temporal extrapolation (prediction), and sensor task as described within the LSM-1 activity.

The utility of LSM-2 embedding is assessed by way of linear probes on many identification duties. Particularly, we consider the applicability of LSM-2 embeddings to binary hypertension classification, binary anxiousness classification, and 20 courses of exercise recognition duties. Assess the flexibility of LSM-2 to mannequin physiology by way of age and BMI regression duties.

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