Non-Bayesian Out-of-Distribution Detection Applied to CNN Architectures for Human Activity Recognition

Abstract: Human Activity Recognition (HAR) field studies the application of artificial intelligence methods for the identification of activities performed by people. Many applications of HAR in healthcare and sports require the safety-critical performance of the predictive models. The predictions produced by these models should be not only correct but also trustworthy. However, in recent years it has been shown that modern neural networks tend to produce sometimes wrong and overconfident predictions when processing unusual inputs. This issue puts at risk the prediction credibility and calls for solutions that might help estimate the uncertainty of the model’s predictions. In the following work, we started the investigation of the applicability of Non-Bayesian Uncertainty Estimation methods to the Deep Learning classification models in the HAR. We trained a Convolutional Neural Network (CNN) model with public datasets, such as UCI HAR and WISDM, which collect sensor-based time-series data about activities of daily life. Through a series of four experiments, we evaluated the performance of two Non-Bayesian uncertainty estimation methods, ODIN and Deep Ensemble, on out-of-distribution detection. We found out that the ODIN method is able to separate out-of-distribution samples from the in-distribution data. However, we also obtained unexpected behavior, when the out-of-distribution data contained exclusively dynamic activities. The Deep Ensemble method did not provide satisfactory results for our research question.