Trapezoidal Segment Sequencing: A Novel Approach for Fusion of Human-Produced Continuous Annotations

Generating accurate ground truth representations of human subjective experiences and judgements is essential for advancing our understanding of human-centered constructs such as emotions. Often, this requires the collection and fusion of annotations from several people where each one is subject to valuation disagreements, distraction artifacts, and other error sources. This work proposes trapezoidal segment sequencing, a new method for fusing annotations into a single representation that, when used alongside a recently proposed signal warping pipeline for correcting annotation artifacts, produces accurate ground truths. We prove that annotations can be well approximated with trapezoidal signals and present results showing the proposed method performs competitively with state-of-the-art fusion methods on a data set where the true target signal being annotated is known. The main utility of the proposed approach is its ability to help segment individual annotations into interpretable regions where either changes or no perceived changes to the construct occur.