用加速度数据训练健康通用模型，实现低功耗设备上的高精度健康预测。
Wearable Accelerometer Foundation Models for Health via Knowledge Distillation
- 通过知识蒸馏，将光电信号模型的知识迁移到加速度信号模型中。
- 在17万参与者数据上训练，心率与心率变异性预测提升23%-49%。
- 适用于任意可穿戴设备，推动数字健康生物标志物发展。
现代可穿戴设备可方便地在日常环境中记录多种生物信号，为个体健康提供丰富视角。然而，并非所有生物信号都同等重要：高保真信号如光电容积脉搏波（PPG）包含更多生理信息，但需功耗较高的光学传感器；而低保真信号如加速度数据功耗极低，几乎存在于所有可穿戴设备中。尽管加速度数据广泛用于活动识别和健身监测，但在健康生物标志物和诊断方面仍较少被探索。本文展示，基于加速度数据的通用模型可预测多种健康目标。为提升性能，我们利用2000万分钟未标注数据（来自约17.2万名参与者的Apple Heart and Movement Study，在知情同意下收集），通过知识蒸馏将PPG编码器的表征知识迁移至加速度编码器。结果显示，在未见数据上跨模态对齐效果显著，例如加速度嵌入检索PPG嵌入的准确率达99.2%。相比直接在加速度数据上自监督或监督训练的编码器，蒸馏后的加速度编码器具有更丰富的表征能力，心率与心率变异性预测性能提升至少23%-49%。此外，蒸馏后的加速度编码器能有效预测多种下游健康目标，表明其具备通用基础模型特性。我们认为，面向健康的加速度基础模型有望从任何可穿戴设备中解锁新型数字生物标志物。
原文摘要 · Abstract (English)
Modern wearable devices can conveniently record various biosignals in the many different environments of daily living, enabling a rich view of individual health. However, not all biosignals are the same: high-fidelity biosignals, such as photoplethysmogram (PPG), contain more physiological information, but require optical sensors with a high power footprint. Alternatively, a lower-fidelity biosignal such as accelerometry has a significantly smaller power footprint and is available in almost any wearable device. While accelerometry is widely used for activity recognition and fitness, it is less explored for health biomarkers and diagnosis. Here, we show that an accelerometry foundation model can predict a wide variety of health targets. To achieve improved performance, we distill representational knowledge from PPG encoders to accelerometery encoders using 20 million minutes of unlabeled data, collected from ~172K participants in the Apple Heart and Movement Study under informed consent. We observe strong cross-modal alignment on unseen data, e.g., 99.2% top-1 accuracy for retrieving PPG embeddings from accelerometry embeddings. We show that distilled accelerometry encoders have significantly more informative representations compared to self-supervised or supervised encoders trained directly on accelerometry data, observed by at least 23%-49% improved performance for predicting heart rate and heart rate variability. We also show that distilled accelerometry encoders are readily predictive of a wide array of downstream health targets, i.e., they are generalist foundation models. We believe accelerometry foundation models for health may unlock new opportunities for developing digital biomarkers from any wearable device.
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