Handling Annotation Uncertainty in Human Activity Recognition

Developing systems for Human Activity Recognition (HAR) using wearables typically relies on datasets that were manually annotated by human experts with regards to precise timings of instances of relevant activities. However, obtaining such data annotations is often very challenging in the predominantly mobile scenarios of Human Activity Recognition. As a result, labels often carry a degree of uncertainty- label jitter-with regards to: i) correct temporal alignments of activity boundaries; and ii) correctness of the actual label provided by the human annotator. In this work, we present a scheme that explicitly incorporates label jitter into the model training process. We demonstrate the effectiveness of the proposed method through a systematic experimental evaluation on standard recognition tasks for which our method leads to significant increases of mean F1 scores.