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).

A Comprehensive Survey on Deep Learning Methods in Human Activity Recognition

“…The experimental dataset, collected at TU Delft, serves as the foundation for investigating the optimal combination of various input features, assessing the impact of the proposed Adaptive Clutter Cancellation (ACC) method, and evaluating the model's robustness within a leave-onesubject-out scenario. In [77], the Bayesian Split Bidirectional recurrent neural network for human activity recognition is introduced, in order to compensate for the computational cost of deep neural networks. The proposed technique harnesses the computational capabilities of the off-premise device to quantify uncertainty, distinguishing between epistemic (uncertainty due to lack of training data) and aleatoric (inherent uncertainty in predictions) uncertainties.…”

A Survey on Radar-Based Continuous Human Activity Recognition

“…To tackle the issue of real-time fall-alerts in hospital environments, Werthen-Brabants et al [58], [59] proposed the use of a split Bi-RNN. A two-stage classifier was implemented: first, a forward RNN which is computed on an edge device gives an immediate prediction for every time step.…”