Micro Visualizations on a Smartwatch: Assessing Reading Performance While Walking

TL;DR

This work investigates how walking motion affects reading micro visualizations on a smartwatch, comparing three trajectories and three speeds ($2$,$4$,$6$ km/h) against sedentary baselines. Two studies quantify reading accuracy, response time, and workload, finding that walking speed, not trajectory, most strongly degrades performance while still enabling high overall accuracy. The results inform design of robust, motion-aware watch-face visualizations and suggest adaptive representations to improve on-the-go data interpretation for fitness and health wearables. Overall, the findings advance understanding of mobile visualization in real-world activity and offer practical guidance for smartwatch UI design under movement.

Abstract

With two studies, we assess how different walking trajectories (straight line, circular, and infinity) and speeds (2 km/h, 4 km/h, and 6 km/h) influence the accuracy and response time of participants reading micro visualizations on a smartwatch. We showed our participants common watch face micro visualizations including date, time, weather information, and four complications showing progress charts of fitness data. Our findings suggest that while walking trajectories did not significantly affect reading performance, overall walking activity, especially at high speeds, hurt reading accuracy and, to some extent, response time.

Figures (2)

• Figure 1: Examples of three watch-face stimuli showing the three radial progress charts: calories burned (cb), step count (sc), and distance walked (dw). The visualized progress percentage on each stimulus are: cb: 75%, dw: 70%, sc: 65% (left), sc: 65%, dw: 60%, cb: 55% (middle), and dw: 50%, cb: 45%, sc: 40% (right).
• Figure 2: Illustrations of the 3 trajectories participants walked in Study 1, from left to right: line, circular, and infinity trajectory.