AI 中文总结
为加速可穿戴健康领域开放科学，发布OpenMHC这一最大最全的可穿戴健康数据集及模型开源实现，引入统一开放基准，对多种模型进行测试，通过大规模开源数据、代码和模型权重，推动可穿戴健康AI研究发展。
AI 中文摘要
移动和可穿戴设备为持续、被动的健康监测和主动健康指导提供了前所未有的机会。然而，最大的可穿戴数据集未公开用于研究，在此类数据集上训练的领先可穿戴基础模型很少是开放权重或有可重现的训练代码。为加速可穿戴健康领域的开放科学，我们发布了OpenMyHeartCounts（OpenMHC），这是迄今为止最大且最全面的开放获取可穿戴健康数据集，以及近期可穿戴基础模型的开源实现。OpenMHC源自通过My Heart Counts研究应用收集的十多年数据，包括超过6000万小时的19个传感器通道的可穿戴数据及多达169个相关变量。此外，我们引入了一个统一的开放基准，用于跨三个轨道对可穿戴健康模型进行标准化比较。我们对经典方法以及近期可穿戴和多变量时间序列基础模型进行基准测试。通过以前所未有的规模开源数据、代码和模型权重，我们旨在使可穿戴健康人工智能研究民主化，并让社区推动该领域的开放进展。
英文摘要
Mobile and wearable devices offer an unprecedented opportunity for continuous, passive health monitoring and active health coaching. However, the largest wearable datasets are not publicly available for research, and leading wearable foundation models trained on such datasets are rarely open-weight or come with reproducible training code. To accelerate open science in wearable health, we release OpenMyHeartCounts (OpenMHC), the largest and most comprehensive broadly accessible wearable health dataset to date, released to qualified researchers, alongside open-source implementations of recent wearable foundation models. OpenMHC, derived from over a decade of data collected through the My Heart Counts study app, includes >60 million hours of wearable data across 19 sensor channels (e.g., step count, heart rate, sleep, workouts) and up to 169 linked variables, including health, lifestyle, mood, and behavior from 11,894 consenting participants. Furthermore, we introduce a unified, open benchmark that enables standardized comparison of wearable health models across three tracks: health and behavior downstream prediction, multivariate data imputation, and time-series forecasting. We benchmark classical methods alongside recent wearable and multivariate time series foundation models. By releasing data under broad research access, alongside open-source code and model weights, at this unprecedented scale, we aim to democratize wearable health AI research and enable the community to drive open progress in this domain.
