虽然无处不在的可穿戴传感器捕获了大量的行为和生理信息,，但将这些信号有效地转化为个性化的健康见解具有挑战性。具体而言，由于表型多样性较高以及个体基线健康,生理学,和生活方式因素的变化，,将低级传感器数据转换为能够表征高级状态的表示是很困难的。此外, 收集与健康结果注释配对的可穿戴数据既费力又昂贵, 并且回顾性注释实际上仍然不可行, 导致了高质量标签数据的稀缺。为了克服这些限制,，我们提出了一种可穿戴健康的基础模型，该模型对来自 500 万参与者的大型群体的超过 1 万亿分钟的未标记传感器信号进行了预训练。我们证明，模型容量和预训练数据量的联合扩展可导致性能, 的系统性改善，这是对 35 个健康预测任务, 的不同集合进行评估，涵盖心血管, 代谢, 睡眠, 和心理健康, 以及生活方式选择和人口因素。我们发现这种群体规模表示解锁了标签高效的小样本学习和生成能力，以实现稳健的日常指标估计。为了进一步利用这种学习到的表示,，我们部署了一个LLM代理教室来自动搜索基于模型嵌入,构建的下游预测头的空间，显示出随着LLM模型容量的增加而广泛的性能改进。最后,，我们展示了如何将这些下游预测因子集成到个人健康代理中，以支持更相关, 上下文感知, 和安全, 的模型响应，并且我们通过一组临床医生的 1,860 评级来验证这一点。

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.

科目:人工智能(cs.AI)

Subjects: Artificial Intelligence (cs.AI)