用可穿戴设备行为数据训练基础模型，提升健康预测效果。
Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions
- 基于25亿小时行为数据训练可穿戴设备基础模型
- 在57项健康任务中表现优异，睡眠预测尤其突出
- 适合做个性化健康分析与动态状态预测的研究者
可穿戴设备记录的生理与行为信号有助于改善健康预测。尽管基础模型在该领域应用日益广泛，但多聚焦于低层次传感器数据，而行为数据因与生理时间尺度更匹配，往往更具信息量。我们利用来自16.2万人、超过25亿小时的可穿戴设备数据，系统优化了模型架构与分词策略，构建了行为信号的基础模型。在57个健康相关任务上评估显示，该模型在个体分类和动态健康状态预测等真实应用场景中表现强劲。在以行为驱动的任务（如睡眠预测）中尤为出色，且结合原始传感器数据表示后性能进一步提升。结果表明，需针对可穿戴设备特性定制基础模型设计，有望开启新型健康应用。
原文摘要 · Abstract (English)
Wearable devices record physiological and behavioral signals that can improve health predictions. While foundation models are increasingly used for such predictions, they have been primarily applied to low-level sensor data, despite behavioral data often being more informative due to their alignment with physiologically relevant timescales and quantities. We develop foundation models of such behavioral signals using over 2.5B hours of wearable data from 162K individuals, systematically optimizing architectures and tokenization strategies for this unique dataset. Evaluated on 57 health-related tasks, our model shows strong performance across diverse real-world applications including individual-level classification and time-varying health state prediction. The model excels in behavior-driven tasks like sleep prediction, and improves further when combined with representations of raw sensor data. These results underscore the importance of tailoring foundation model design to wearables and demonstrate the potential to enable new health applications.
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