论文标题

Cozie Apple:iOS移动和智能手表应用程序,用于环境质量满意度和生理数据收集

Cozie Apple: An iOS mobile and smartwatch application for environmental quality satisfaction and physiological data collection

论文作者

Tartarini, Federico, Frei, Mario, Schiavon, Stefano, Chua, Yun Xuan, Miller, Clayton

论文摘要

传统上,以可靠,纵向和非侵扰的方式收集室内和室外环境中人们的反馈是充满挑战和复杂的。本文推出了Cozie Apple,这是一种用于iOS设备的开源移动和智能手表应用程序。该平台允许人们完成基于手表的微观调查,并通过其Apple Watch提供有关环境条件的实时反馈。它利用智能手表的内置传感器来收集生理(例如,心率,活动)和环境(声音级别)数据。本文概述了从48位使用该平台报告城市规模环境舒适度(噪声和热力)以及情境因素(例如与谁和他们在做什么活动)的看法的研究参与者收集的数据。本文中说明了各种城市环境中2,400个微型群体的结果,显示了与噪声相关的干扰,热舒适性和相关环境的可变性。结果表明,人们至少会在58%的时间内经历一些噪音分散注意力,而人们说的是最常见的原因(46%)。这项工作是新颖的,因为它专注于空间和时间的可扩展性以及噪音,分心和相关上下文信息的收集。这些数据为更大的部署,更深入的分析以及更有帮助的预测模型奠定了基础,以更好地理解居住者的需求和看法。这些创新可能会导致实时控制信号,以使人们改变其行为。

Collecting feedback from people in indoor and outdoor environments is traditionally challenging and complex in a reliable, longitudinal, and non-intrusive way. This paper introduces Cozie Apple, an open-source mobile and smartwatch application for iOS devices. This platform allows people to complete a watch-based micro-survey and provide real-time feedback about environmental conditions via their Apple Watch. It leverages the inbuilt sensors of a smartwatch to collect physiological (e.g., heart rate, activity) and environmental (sound level) data. This paper outlines data collected from 48 research participants who used the platform to report perceptions of urban-scale environmental comfort (noise and thermal) and contextual factors such as who they were with and what activity they were doing. The results of 2,400 micro-surveys across various urban settings are illustrated in this paper showing the variability of noise-related distractions, thermal comfort, and associated context. The results show people experience at least a little noise distraction 58% of the time, with people talking being the most common reason (46%). This effort is novel due to its focus on spatial and temporal scalability and collection of noise, distraction, and associated contextual information. These data set the stage for larger deployments, deeper analysis, and more helpful prediction models toward better understanding the occupants' needs and perceptions. These innovations could result in real-time control signals to building systems or nudges for people to change their behavior.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源