Quantifying Uncertainty in Real Time with Split BiRNN for Radar Human Activity Recognition

Abstract

Radar systems can be used to perform human activity recognition in a privacy preserving manner. Deep Neural Networks are able to effectively process the complex radar data and make predictions. Often these networks are large and do not scale well when processing a large amount of radar streams at once, for example when monitoring multiple rooms in a hospital. This work proposes Bayesian Split Bidirectional Recurrent Neural Network for Human Activity Recognition. Using this technique the processing of data is split in two parts, one part on-premise (low-power, low-cost device), and the other off-premise (high power device). The proposed approach leverages the power of the off-premise device to quantify its uncertainty, and to gain more information on its epistemic and its aleatoric parts. Results indicate the proposed approach is able to correctly identify parts of a prediction that either need more training data for better predictions (epistemic uncertainty), or are inherently hard to classify by the model (aleatoric uncertainty).