这项研究解决了可穿戴数据标注稀缺和个体差异大的核心难题，做健康AI或可穿戴设备开发的团队可以直接参考其预训练方法和少样本学习策略，值得关注。
研究人员提出了一种面向可穿戴健康数据的基础模型，该模型在来自500万参与者的超过1万亿分钟未标记传感器信号上进行了预训练。通过联合扩展模型容量和预训练数据量，该模型在35项健康预测任务上（涵盖心血管、代谢、睡眠、心理健康及生活方式等）表现出系统性性能提升。该模型支持少样本学习和生成能力，可稳健估计日常健康指标。研究还部署了一组LLM智能体来自动搜索基于模型嵌入的下游预测头，并展示了性能随LLM能力提升而增强。最后，将下游预测器集成到个人健康代理中，经1860次临床医生评分验证，模型响应更相关、更具上下文意识且更安全。
Towards a General Intelligence and Interface for Wearable Health Data
While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personalized health insights is challenging. Specifically, converting low-level sensor data into representations capable of characterizing higher-level states is difficult due to high phenotypic diversity and variation in individual baseline health, physiology, and lifestyle factors. Moreover, collecting wearable data paired with health outcome annotations is laborious and expensive, and retrospective annotation remains practically unfeasible, contributing to a scarcity of data with high-quality labels. To overcome these limitations, we propose a foundation model for wearable health that is pretrained on more than one trillion minutes of unlabeled sensor signals drawn from a large cohort of five million participants. We demonstrate that the joint scaling of model capacity and pretraining data volume leads to systematic improvements in performance, as evaluated on a diverse set of 35 health prediction tasks, spanning cardiovascular, metabolic, sleep, and mental health, as well as lifestyle choices and demographic factors. We find that this population scale representation unlocks label-efficient few-shot learning and generative capabilities for robust daily metric estimation. To further leverage this learned representation, we deploy a classroom of LLM agents to autonomously search the space of downstream predictive heads built on the model embeddings, showing broad performance improvements that increase with LLM model capacity. Finally, we show how integrating these downstream predictors into a Personal Health Agent can support model responses that are more relevant, contextually aware, and safe, and we validate this via 1,860 ratings from a cohort of clinicians.