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.

翻译：在室内和室外环境中以可靠、纵向且非侵入的方式收集人们的反馈，传统上既具挑战性又复杂。本文介绍了Cozie Apple，一款面向iOS设备的开源移动及智能手表应用。该平台允许用户通过Apple Watch完成基于手表的微调查，并实时反馈对环境条件的感受。它利用智能手表内置传感器收集生理数据（如心率、活动量）和环境数据（如声级）。本文概述了来自48名研究参与者的数据，他们使用该平台报告对城市尺度环境舒适度（噪声与热舒适）以及上下文因素（如同伴及当前活动）的感知。本文展示了涵盖多种城市环境的2400份微调查结果，揭示了噪声干扰、热舒适及相关背景的变异性。结果表明，人们在58%的时间内至少受到轻度噪声干扰，其中人声交谈是最常见的原因（占46%）。本研究的创新之处在于聚焦于时空可扩展性，以及同时收集噪声、干扰及相关的背景信息。这些数据为更大规模的部署、更深入的分析以及更有效的预测模型奠定了基础，有助于更好地理解用户的需求与感知。这些创新成果可转化为建筑系统的实时控制信号，或引导人们改变自身行为的提示。