Abstract

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 …

Date

2019

Authors

Tiantian Feng, Shrikanth Narayanan

Conference

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Pages

2529-2534

Publisher

IEEE