![]() ![]() Without a doubt, synthetic imagery bears a vast potential due to scalability in terms of amounts of data obtainable without tedious manual ground truth annotations or measurements. ![]() We envision our multi-year datasets can support the ML community in developing generalizable longitudinal behavior modeling algorithms. Datasets have gained an enormous amount of popularity in the computer vision community, from training and evaluation of Deep Learning-based methods to benchmarking Simultaneous Localization and Mapping (SLAM). 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. As a starting point, we provide the benchmark results of 18 algorithms on the task of depression detection. Our datasets can support multiple cross-dataset evaluations of behavior modeling algorithms’ generalizability across different users and years. 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. ![]() 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. However, there is a lack of a comprehensive public dataset that serves as an open testbed for fair comparison among algorithms. Abstract: Recent research has demonstrated the capability of behavior signals captured by smartphones and wearables for longitudinal behavior modeling. ![]()
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