Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data
نویسندگان
چکیده
We analyse multimodal time-series data corresponding to weight, sleep and steps measurements, spanning 15000 users, collected across consumer-grade health devices by Nokia Digital Health Withings. We focus on predicting whether a user will successfully achieve their weight objective. For this, we design several deep recurrent architectures, including a novel cross-modal LSTM (X-LSTM), and demonstrate their superiority over baseline approaches. Scaling to even thousands of users limits the kind of data that sufficiently many user devices can accurately measure—therefore, many factors key in weight change (such as eating habits) must remain latently observed.
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