Smartwatches rely on glanceable visualizations to effectively convey information within limited screen space. This thesis aims to investigates the use of three pre-attentive visual variables - color, area, and motion - to highlight data points in common smartwatch graphs. We conducted a user study with 48 participants, evaluating four chart types: bar chart, line chart, linear progress chart, and radial progress chart. To assess whether pre-attentive processing can be leveraged for micro-visualization, we set stimulus exposure to 250 ms and examined perception under sedentary and walking conditions to determine effectiveness in mobile scenarios. Our results show that all three visual variables can be perceived with high accuracy, though performance varies depending on chart complexity. While simpler visualizations support effective highlighting, accuracy declines in more detailed time-series charts. Among the visual variables, motion and color yield the best perception rates, while area proves to be the least effective. These findings contribute to the design of efficient smartwatch visualizations that support rapid and intuitive data recognition.