穿戴设备厂商和健康 AI 研究者终于有了一个通用基础模型，不用再为每个健康任务单独设计特征工程。做可穿戴健康分析的团队可以直接参考 SensorFM 的预训练思路，大幅降低模型开发成本。
Google 发表新论文，提出 SensorFM，一个基于 500 万人超过 1 万亿分钟未标记穿戴传感器数据训练的基础模型。该模型旨在学习人类生理活动的通用模式，而非仅处理孤立事件。SensorFM 在 35 项预测任务中的 34 项上超越了传统特征工程方法，涵盖心血管、代谢、心理健康、睡眠和生活方式等领域。研究表明，穿戴数据的价值在于先学习其内在结构，而非过早压缩为粗略摘要。
New Google paper shows that wearable data becomes …
New Google paper shows that wearable data becomes far more useful when AI learns the person behind the signals.
It's is not another heart-rate algorithm, but a general model trained on more than one trillion minutes of sensor data from five million people.
The authors propose SensorFM, a foundation model trained on more than 1 trillion minutes of unlabeled wearable data from 5 million people, so it can learn general patterns of human physiology before seeing specific health tasks.
That scale changes the problem from measuring isolated events to learning patterns of lived physiology: sleep, movement, temperature, oxygen, heart rhythms, and their ordinary daily messiness.
Wearables are not weak because they lack data; they are weak because most systems compress that data into crude summaries before the meaningful structure has a chance to appear.
SensorFM tries to learn that structure first, then reuse it across tasks, which is why the same representation can help with cardiovascular, metabolic, mental health, sleep, lifestyle, and demographic predictions.
The evidence is strongest as a scaling story: larger models trained on more data performed better, and the learned embeddings beat engineered-feature baselines on 34 of 35 prediction tasks.
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Paper Link – arxiv. org/abs/2511.15352v3
Paper Title: "People readily follow personal advice from AI but it does not improve their well-being"