Sounds of Health: Using Personalized Sonification Models to Communicate Health Information
Sounds of Health: Using Personalized Sonification Models to Communicate Health Information
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健康之声：使用个性化的发声模型来传达健康信息
DOI：
10.1145/3570346
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Doryab, Afsaneh
中科院分区：
文献类型：
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作者：
Clark, Matthew;Doryab, Afsaneh
本文探讨了在个人设备上使用发声来传递和传达健康和健康状态的可行性。事实证明，环境显示可以告知用户他们的健康状况，并帮助他们做出更健康的决定，然而，很少有技术通过声音提供健康评估，这可能比视觉显示更普遍。我们开发了一种根据用户偏好生成音乐的方法，并在两步用户研究中对其进行了评估。在第一步中，我们从每个用户那里获得了一般的健康印象。在第二步中，我们根据第一步中的音乐偏好生成定制的旋律，以捕捉参与者对这些旋律的感知健康程度。我们部署了我们的调查，让55名参与者在31天内自行完成。我们分析了这些数据，以了解用户对音乐作为健康表达的看法的共性和差异。我们的发现表明，感知的健康和不同的音乐特征之间存在明显的关联。我们提供了关于不同音乐特征如何影响感知的音乐健康、用户对健康的感知如何不同、用户印象之间存在什么趋势以及什么影响(或不影响)用户在旋律中的健康感知的有用见解。总体而言，我们的结果表明，通过个性化音乐模型呈现健康数据是有效的。这一发现可以为个人和无处不在的设备上的行为管理应用程序的设计提供参考。
This paper explores the feasibility of using sonification in delivering and communicating health and wellness status on personal devices. Ambient displays have proven to inform users of their health and wellness and help them to make healthier decisions, yet, little technology provides health assessments through sounds, which can be even more pervasive than visual displays. We developed a method to generate music from user preferences and evaluated it in a two-step user study. In the first step, we acquired general healthiness impressions from each user. In the second step, we generated customized melodies from music preferences in the first step to capture participants' perceived healthiness of those melodies. We deployed our surveys for 55 participants to complete on their own over 31 days. We analyzed the data to understand commonalities and differences in users' perceptions of music as an expression of health. Our findings show the existence of clear associations between perceived healthiness and different music features. We provide useful insights into how different musical features impact the perceived healthiness of music, how perceptions of healthiness vary between users, what trends exist between users' impressions, and what influences (or does not influence) a user's perception of healthiness in a melody. Overall, our results indicate validity in presenting health data through personalized music models. The findings can inform the design of behavior management applications on personal and ubiquitous devices.