This study delves into the nuanced interplay between the motion features of hand-drawn lines and their capacity to convey emotions, a relatively underexplored facet within the realm of human-computer interaction and visual art. By initiating an original experimental design, we generated a pioneering dataset, capturing both static and motion features of lines drawn to express a spectrum of emotions. Through meticulous analysis employing multivariate ordered logistic regression, we unearthed significant motion features that significantly influence emotional expression, alongside corroborating the relevance of certain static features. Our investigation extends beyond mere feature identification, exploring how these attributes correlate with emotional perceptions across a broad emotional spectrum. This research not only bridges a gap in existing literature but also lays foundational insights for future explorations into the emotional dimensions of visual art and design, offering new perspectives for enhancing creative processes and understanding the art-emotion nexus.