![]() We envision our multi-year datasets can support the ML community in developing generalizable longitudinal behavior modeling algorithms. 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. To address this problem, we present an open Multi-Sensor All Weather Mapping (MSAW) dataset and challenge, which features two collection modalities (both SAR and optical). This dataset features a unique combination of half-meter quad-polarized X-band SAR imagery (Figure ) and half-meter optical imagery over the port of Rotterdam, the Netherlands. 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. the Multi-Sensor All Weather Mapping (MSAW) dataset. 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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