Personal heart rate data from wearable devices contains rich information, yet current visualizations primarily focus on simple metrics, leaving complex temporal patterns largely unexplored. We present a speculative exploration of personal heart rate visualization possibilities through five prototype approaches derived from established visualization literature: pattern/variability heatmaps, recurrence plots, spectrograms, T-SNE, and Poincaré plots. Using physiologically-informed synthetic datasets generated through large language models, we systematically explore how different visualization strategies might reveal distinct aspects of heart rate patterns across temporal scales and analytical complexity. We evaluate these prototypes using established visualization assessment scales from multiple literacy perspectives, then conduct reflective analysis on both the evaluation and the design of the prototypes. Our iterative process reveals recurring design tensions in visualizing complex physiological data. This work offers a speculative map of the personal heart rate visualization design space, providing insights into making heart rate data more visually accessible and meaningful.

翻译：来自可穿戴设备的个人心率数据蕴含丰富信息，然而当前的可视化方法主要聚焦于简单指标，复杂的时序模式在很大程度上尚未得到充分探索。我们通过源自成熟可视化文献的五种原型方法——模式/变异性热力图、递归图、频谱图、T-SNE和庞加莱图——对个人心率可视化的可能性进行了推测性探索。利用基于大型语言模型生成的生理学合成数据集，我们系统性地研究了不同可视化策略如何揭示跨时间尺度和分析复杂度的心率模式的不同方面。我们采用多维度素养视角下的成熟可视化评估量表对这些原型进行评估，并对评估过程及原型设计进行了反思性分析。我们的迭代过程揭示了在可视化复杂生理数据时反复出现的设计张力。本研究为个人心率可视化设计空间提供了一幅推测性图谱，为提升心率数据的视觉可访问性与意义解读提供了见解。