Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data
This work addresses a domain-specific problem in health monitoring, offering incremental improvements for predicting weight objectives from multimodal data.
The paper tackles the problem of predicting whether a user will achieve a weight objective using multimodal time-series data (weight, sleep, steps) by designing deep LSTM architectures, including a novel cross-modal LSTM (X-LSTM), and demonstrates its superiority over baseline approaches with improved parameter efficiency.
We analyse multimodal time-series data corresponding to weight, sleep and steps measurements. We focus on predicting whether a user will successfully achieve his/her weight objective. For this, we design several deep long short-term memory (LSTM) architectures, including a novel cross-modal LSTM (X-LSTM), and demonstrate their superiority over baseline approaches. The X-LSTM improves parameter efficiency by processing each modality separately and allowing for information flow between them by way of recurrent cross-connections. We present a general hyperparameter optimisation technique for X-LSTMs, which allows us to significantly improve on the LSTM and a prior state-of-the-art cross-modal approach, using a comparable number of parameters. Finally, we visualise the model's predictions, revealing implications about latent variables in this task.