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.
@article{arxiv.2211.02733,
title = {GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization},
author = {Xuhai Xu and Han Zhang and Yasaman Sefidgar and Yiyi Ren and Xin Liu and Woosuk Seo and Jennifer Brown and Kevin Kuehn and Mike Merrill and Paula Nurius and Shwetak Patel and Tim Althoff and Margaret E. Morris and Eve Riskin and Jennifer Mankoff and Anind K. Dey},
journal= {arXiv preprint arXiv:2211.02733},
year = {2023}
}
Comments
Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track