Imputing Missing Data In Large-Scale Multivariate Biomedical Wearable Recordings Using Bidirectional Recurrent Neural Networks With Temporal Activation Regularization.

Miniaturized and wearable sensor-based measurements offer unprecedented opportunities to study and assess human behavior in natural settings with wide ranging applications including in healthcare, wellness tracking and entertainment. However, wearable sensors are vulnerable to data loss due to body movement, sensor displacement, software malfunctions, etc. This generally hinders advanced data analytics including for clustering, data summarization, and pattern recognition requiring robust solutions for handling missing data to obtain accurate and unbiased analysis. Conventional data imputation strategies to address the challenges of missing data, including statistical fill-in, matrix factorization and traditional machine learning approaches, are inadequate in capturing temporal variations in multivariate time series. In this paper, we investigate data imputation using bidirectional recurrent neural networks with temporal activation regularization, which can directly learn and fill in the missing data. We evaluate the method on a large-scale multimodal wearable recording data-set of bio-behavioral signals we recently collected from over 100 hospital staff for a period of 10 weeks. Experimental results on these multimodal time series show the superiority of the proposed RNN-based method in terms of imputation accuracy.