Recent research has demonstrated the capability of behavior signals captured by smartphones and wearables for longitudinal behavior modeling. However, there is a lack of a comprehensive public dataset that serves as an open testbed for fair comparison among algorithms. Moreover, prior studies mainly evaluate algorithms using data from a single population within a short period, without measuring the cross-dataset generalizability of these algorithms. We present the first multi-year passive sensing datasets, containing over 700 user-years and 497 unique users' data collected from mobile and wearable sensors, together with a wide range of well-being metrics. Our datasets can support multiple cross-dataset evaluations of behavior modeling algorithms' generalizability across different users and years. As a starting point, we provide the benchmark results of 18 algorithms on the task of depression detection. Our results indicate that both prior depression detection algorithms and domain generalization techniques show potential but need further research to achieve adequate cross-dataset generalizability. We envision our multi-year datasets can support the ML community in developing generalizable longitudinal behavior modeling algorithms.

翻译：近期研究已证明了智能手机和可穿戴设备捕获的行为信号在纵向行为建模中的能力。然而，目前仍缺乏一个可作为算法公平比较开放测试平台的综合性公共数据集。此外，先前研究主要基于短时期内单一群体数据评估算法性能，未衡量这些算法的跨数据集泛化能力。本文首次提出包含超过700个用户年、497名独立用户数据的多年被动感知数据集，这些数据来自移动与可穿戴传感器，并涵盖多种健康指标。本数据集可支撑跨不同用户与年份的行为建模算法泛化能力的多项跨数据集评估。作为起点，我们提供了18种算法在抑郁检测任务上的基准结果。结果表明，既有抑郁检测算法与领域泛化技术虽展现出潜力，但需进一步研究以实现充分的跨数据集泛化能力。我们期望本多年数据集能助力机器学习社区开发可泛化的纵向行为建模算法。