构建最大可穿戴传感器基础模型，实现高效数据补全与动作识别。
Scaling Wearable Foundation Models
- 基于4000万小时多模态传感器数据训练大模型
- 在时间与传感器维度上实现精准插值外推
- 支持少样本下游任务，适合健康监测应用
可穿戴设备因丰富的健康追踪功能已广泛应用。日常生活中持续生成的海量生理数据虽具价值，但从中提取科学与可操作洞察仍具挑战。受生成建模成功启发，我们研究了传感器基础模型在计算、数据和模型规模上的扩展规律。利用来自超过16.5万人的4000万小时心率、心率变异性、皮肤电活动、加速度计、皮肤温度和气压计等每分钟数据，我们构建了LSM——目前最大且涵盖最广传感器模态的多模态基础模型。结果表明，该模型在数据补全、插值与外推任务中具有明确的缩放规律。此外，其支持高效少样本下游学习，在运动与活动识别等任务中表现优异。
原文摘要 · Abstract (English)
Wearable sensors have become ubiquitous thanks to a variety of health tracking features. The resulting continuous and longitudinal measurements from everyday life generate large volumes of data; however, making sense of these observations for scientific and actionable insights is non-trivial. Inspired by the empirical success of generative modeling, where large neural networks learn powerful representations from vast amounts of text, image, video, or audio data, we investigate the scaling properties of sensor foundation models across compute, data, and model size. Using a dataset of up to 40 million hours of in-situ heart rate, heart rate variability, electrodermal activity, accelerometer, skin temperature, and altimeter per-minute data from over 165,000 people, we create LSM, a multimodal foundation model built on the largest wearable-signals dataset with the most extensive range of sensor modalities to date. Our results establish the scaling laws of LSM for tasks such as imputation, interpolation and extrapolation, both across time and sensor modalities. Moreover, we highlight how LSM enables sample-efficient downstream learning for tasks like exercise and activity recognition.
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